If you think that there is an error in how your package is being tested or represented, please file an issue at NewPkgEval.jl, making sure to read the FAQ first.
Resolving package versions...
Installed Compat ─────────────── v2.2.0
Installed ReverseDiffSparse ──── v0.8.6
Installed DiffResults ────────── v0.0.4
Installed DiffRules ──────────── v0.1.0
Installed DataStructures ─────── v0.17.6
Installed Alpine ─────────────── v0.1.10
Installed BinaryProvider ─────── v0.5.8
Installed JuMP ───────────────── v0.18.6
Installed ForwardDiff ────────── v0.10.7
Installed CommonSubexpressions ─ v0.2.0
Installed OrderedCollections ─── v1.1.0
Installed SpecialFunctions ───── v0.8.0
Installed MathProgBase ───────── v0.7.7
Installed StaticArrays ───────── v0.12.1
Installed Calculus ───────────── v0.5.1
Installed NaNMath ────────────── v0.3.3
Installed URIParser ──────────── v0.4.0
Installed BinDeps ────────────── v0.8.10
Updating `~/.julia/environments/v1.2/Project.toml`
[07493b3f] + Alpine v0.1.10
Updating `~/.julia/environments/v1.2/Manifest.toml`
[07493b3f] + Alpine v0.1.10
[9e28174c] + BinDeps v0.8.10
[b99e7846] + BinaryProvider v0.5.8
[49dc2e85] + Calculus v0.5.1
[bbf7d656] + CommonSubexpressions v0.2.0
[34da2185] + Compat v2.2.0
[864edb3b] + DataStructures v0.17.6
[163ba53b] + DiffResults v0.0.4
[b552c78f] + DiffRules v0.1.0
[f6369f11] + ForwardDiff v0.10.7
[4076af6c] + JuMP v0.18.6
[fdba3010] + MathProgBase v0.7.7
[77ba4419] + NaNMath v0.3.3
[bac558e1] + OrderedCollections v1.1.0
[89212889] + ReverseDiffSparse v0.8.6
[276daf66] + SpecialFunctions v0.8.0
[90137ffa] + StaticArrays v0.12.1
[30578b45] + URIParser v0.4.0
[2a0f44e3] + Base64
[ade2ca70] + Dates
[8bb1440f] + DelimitedFiles
[8ba89e20] + Distributed
[b77e0a4c] + InteractiveUtils
[76f85450] + LibGit2
[8f399da3] + Libdl
[37e2e46d] + LinearAlgebra
[56ddb016] + Logging
[d6f4376e] + Markdown
[a63ad114] + Mmap
[44cfe95a] + Pkg
[de0858da] + Printf
[3fa0cd96] + REPL
[9a3f8284] + Random
[ea8e919c] + SHA
[9e88b42a] + Serialization
[1a1011a3] + SharedArrays
[6462fe0b] + Sockets
[2f01184e] + SparseArrays
[10745b16] + Statistics
[8dfed614] + Test
[cf7118a7] + UUIDs
[4ec0a83e] + Unicode
Building SpecialFunctions → `~/.julia/packages/SpecialFunctions/ne2iw/deps/build.log`
Testing Alpine
Resolving package versions...
Installed Ipopt ─────────────────── v0.6.1
Installed GLPKMathProgInterface ─── v0.4.4
Installed ConicBenchmarkUtilities ─ v0.3.1
Installed Pavito ────────────────── v0.1.2
Installed Cbc ───────────────────── v0.6.6
Installed MathOptInterface ──────── v0.9.7
Installed Parsers ───────────────── v0.3.10
Installed BenchmarkTools ────────── v0.4.3
Installed JSON ──────────────────── v0.21.0
Installed ConicNonlinearBridge ──── v0.2.1
Installed GZip ──────────────────── v0.5.1
Installed GLPK ──────────────────── v0.12.0
Building Cbc ──→ `~/.julia/packages/Cbc/vWzyC/deps/build.log`
Building Ipopt → `~/.julia/packages/Ipopt/ruIXY/deps/build.log`
Building GLPK ─→ `~/.julia/packages/GLPK/J1b5G/deps/build.log`
Status `/tmp/jl_dHDMj5/Manifest.toml`
[07493b3f] Alpine v0.1.10
[6e4b80f9] BenchmarkTools v0.4.3
[9e28174c] BinDeps v0.8.10
[b99e7846] BinaryProvider v0.5.8
[49dc2e85] Calculus v0.5.1
[9961bab8] Cbc v0.6.6
[bbf7d656] CommonSubexpressions v0.2.0
[34da2185] Compat v2.2.0
[e95a7839] ConicBenchmarkUtilities v0.3.1
[952205b0] ConicNonlinearBridge v0.2.1
[864edb3b] DataStructures v0.17.6
[163ba53b] DiffResults v0.0.4
[b552c78f] DiffRules v0.1.0
[f6369f11] ForwardDiff v0.10.7
[60bf3e95] GLPK v0.12.0
[3c7084bd] GLPKMathProgInterface v0.4.4
[92fee26a] GZip v0.5.1
[b6b21f68] Ipopt v0.6.1
[682c06a0] JSON v0.21.0
[4076af6c] JuMP v0.18.6
[b8f27783] MathOptInterface v0.9.7
[fdba3010] MathProgBase v0.7.7
[77ba4419] NaNMath v0.3.3
[bac558e1] OrderedCollections v1.1.0
[69de0a69] Parsers v0.3.10
[cd433a01] Pavito v0.1.2
[89212889] ReverseDiffSparse v0.8.6
[276daf66] SpecialFunctions v0.8.0
[90137ffa] StaticArrays v0.12.1
[30578b45] URIParser v0.4.0
[2a0f44e3] Base64 [`@stdlib/Base64`]
[ade2ca70] Dates [`@stdlib/Dates`]
[8bb1440f] DelimitedFiles [`@stdlib/DelimitedFiles`]
[8ba89e20] Distributed [`@stdlib/Distributed`]
[b77e0a4c] InteractiveUtils [`@stdlib/InteractiveUtils`]
[76f85450] LibGit2 [`@stdlib/LibGit2`]
[8f399da3] Libdl [`@stdlib/Libdl`]
[37e2e46d] LinearAlgebra [`@stdlib/LinearAlgebra`]
[56ddb016] Logging [`@stdlib/Logging`]
[d6f4376e] Markdown [`@stdlib/Markdown`]
[a63ad114] Mmap [`@stdlib/Mmap`]
[44cfe95a] Pkg [`@stdlib/Pkg`]
[de0858da] Printf [`@stdlib/Printf`]
[3fa0cd96] REPL [`@stdlib/REPL`]
[9a3f8284] Random [`@stdlib/Random`]
[ea8e919c] SHA [`@stdlib/SHA`]
[9e88b42a] Serialization [`@stdlib/Serialization`]
[1a1011a3] SharedArrays [`@stdlib/SharedArrays`]
[6462fe0b] Sockets [`@stdlib/Sockets`]
[2f01184e] SparseArrays [`@stdlib/SparseArrays`]
[10745b16] Statistics [`@stdlib/Statistics`]
[8dfed614] Test [`@stdlib/Test`]
[cf7118a7] UUIDs [`@stdlib/UUIDs`]
[4ec0a83e] Unicode [`@stdlib/Unicode`]
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
PROBLEM STATISTICS
# of constraints = 6
# of non-linear constraints = 3
# of linear constraints = 3
# of continuous variables = 8
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 5
# of variables involved in nonlinear terms = 8
# of potential variables for partitioning = 8
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 1
Relative optimality gap criteria = 0.0100%
Potential variables chosen for partitioning = All
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
******************************************************************************
This program contains Ipopt, a library for large-scale nonlinear optimization.
Ipopt is released as open source code under the Eclipse Public License (EPL).
For more information visit http://projects.coin-or.org/Ipopt
******************************************************************************
Local solver returns a feasible point
Completed presolve in 1.71s (0 iterations).
====================================================================================================
LOWER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
| finish | 7049.2479 | 7049.2479 | 3004.247 | 57.38202 | 5.26s
====================================================================================================
*** Alpine ended with status UserLimits ***
┌ Warning: Not solved to optimality, status: UserLimits
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/nlp.jl:1283
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
PROBLEM STATISTICS
# of constraints = 6
# of non-linear constraints = 3
# of linear constraints = 3
# of continuous variables = 8
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 5
# of variables involved in nonlinear terms = 8
# of potential variables for partitioning = 8
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 1
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.02s (0 iterations).
====================================================================================================
LOWER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
| finish | 7049.2479 | 7049.2479 | 3004.247 | 57.38202 | 0.12s
====================================================================================================
*** Alpine ended with status UserLimits ***
┌ Warning: Not solved to optimality, status: UserLimits
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/nlp.jl:1283
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
PROBLEM STATISTICS
# of constraints = 6
# of non-linear constraints = 3
# of linear constraints = 3
# of continuous variables = 8
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 5
# of variables involved in nonlinear terms = 8
# of potential variables for partitioning = 3
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 1
Relative optimality gap criteria = 0.0100%
Potential variables chosen for partitioning = Min. vertex cover
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.02s (0 iterations).
====================================================================================================
LOWER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
| finish | 7049.2479 | 7049.2479 | 2543.1331 | 63.92334 | 0.05s
====================================================================================================
*** Alpine ended with status UserLimits ***
┌ Warning: Not solved to optimality, status: UserLimits
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/nlp.jl:1283
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
PROBLEM STATISTICS
# of constraints = 6
# of non-linear constraints = 3
# of linear constraints = 3
# of continuous variables = 8
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 5
# of variables involved in nonlinear terms = 8
# of potential variables for partitioning = 8
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 2
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.02s (0 iterations).
====================================================================================================
LOWER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
| 1 | 7049.2479 | 7049.2479 | 3004.247 | 57.38202 | 0.11s
| finish | - | 7049.2479 | 4896.6075 | 30.53716 | 0.55s
====================================================================================================
*** Alpine ended with status UserLimits ***
┌ Warning: Not solved to optimality, status: UserLimits
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/nlp.jl:1283
Test Summary: | Pass Total
Partitioning variable selection tests :: nlp3 | 20 20
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Warning: -/+Inf bounds detected on at least 1 variable. Initializing with values -/+10000.0. This may affect global optimality and run times.
PROBLEM STATISTICS
# of constraints = 45
# of non-linear constraints = 12
# of linear constraints = 33
# of continuous variables = 42
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 12
# of variables involved in nonlinear terms = 10
# of potential variables for partitioning = 10
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 1
Relative optimality gap criteria = 0.0100%
Potential variables chosen for partitioning = All
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.1s (0 iterations).
====================================================================================================
LOWER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
| finish | 470.3175 | 470.3175 | 78.0 | 83.41546 | 0.22s
====================================================================================================
*** Alpine ended with status UserLimits ***
┌ Warning: Not solved to optimality, status: UserLimits
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/nlp.jl:1283
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Warning: -/+Inf bounds detected on at least 1 variable. Initializing with values -/+10000.0. This may affect global optimality and run times.
PROBLEM STATISTICS
# of constraints = 45
# of non-linear constraints = 12
# of linear constraints = 33
# of continuous variables = 42
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 12
# of variables involved in nonlinear terms = 10
# of potential variables for partitioning = 4
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 1
Relative optimality gap criteria = 0.0100%
Potential variables chosen for partitioning = Min. vertex cover
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.13s (0 iterations).
====================================================================================================
LOWER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
| finish | 470.3175 | 470.3175 | 78.0 | 83.41546 | 0.15s
====================================================================================================
*** Alpine ended with status UserLimits ***
┌ Warning: Not solved to optimality, status: UserLimits
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/nlp.jl:1283
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Warning: -/+Inf bounds detected on at least 1 variable. Initializing with values -/+10000.0. This may affect global optimality and run times.
PROBLEM STATISTICS
# of constraints = 45
# of non-linear constraints = 12
# of linear constraints = 33
# of continuous variables = 42
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 12
# of variables involved in nonlinear terms = 10
# of potential variables for partitioning = 10
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 1
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.08s (0 iterations).
====================================================================================================
LOWER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
| finish | 470.3175 | 470.3175 | 78.0 | 83.41546 | 0.1s
====================================================================================================
*** Alpine ended with status UserLimits ***
┌ Warning: Not solved to optimality, status: UserLimits
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/nlp.jl:1283
Test Summary: | Pass Total
Partitioning variable selection tests :: castro2m2 | 15 15
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 225
# of non-linear constraints = 24
# of linear constraints = 201
# of continuous variables = 66
# of binary variables = 36
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 28
# of variables involved in nonlinear terms = 26
# of potential variables for partitioning = 26
=======================================================================
SUB-SOLVERS USED BY ALPINE
MINLP local solver = Pavito
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 1
Relative optimality gap criteria = 0.0100%
Potential variables chosen for partitioning = All
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
PROBLEM STATISTICS
# of constraints = 225
# of non-linear constraints = 24
# of linear constraints = 201
# of continuous variables = 66
# of binary variables = 36
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 28
# of variables involved in nonlinear terms = 26
# of potential variables for partitioning = 10
=======================================================================
SUB-SOLVERS USED BY ALPINE
MINLP local solver = Pavito
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 1
Relative optimality gap criteria = 0.0100%
Potential variables chosen for partitioning = Min. vertex cover
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
PROBLEM STATISTICS
# of constraints = 225
# of non-linear constraints = 24
# of linear constraints = 201
# of continuous variables = 66
# of binary variables = 36
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 28
# of variables involved in nonlinear terms = 26
# of potential variables for partitioning = 10
=======================================================================
SUB-SOLVERS USED BY ALPINE
MINLP local solver = Pavito
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 1
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
Test Summary: | Pass Total
Partitioning variable selection tests :: blend029 | 12 12
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
Warning: -/+Inf bounds detected on at least 40 variables. Initializing with values -/+10000.0. This may affect global optimality and run times.
PROBLEM STATISTICS
# of constraints = 152
# of non-linear constraints = 48
# of linear constraints = 104
# of continuous variables = 174
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 48
# of variables involved in nonlinear terms = 24
# of potential variables for partitioning = 12
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 1
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.77s (0 iterations).
====================================================================================================
LOWER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
| finish | 227.981 | 227.981 | 95.2208 | 58.23302 | 0.89s
====================================================================================================
*** Alpine ended with status UserLimits ***
┌ Warning: Not solved to optimality, status: UserLimits
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/nlp.jl:1283
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
Warning: -/+Inf bounds detected on at least 40 variables. Initializing with values -/+10000.0. This may affect global optimality and run times.
PROBLEM STATISTICS
# of constraints = 152
# of non-linear constraints = 48
# of linear constraints = 104
# of continuous variables = 174
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 48
# of variables involved in nonlinear terms = 24
# of potential variables for partitioning = 12
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 2
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.78s (0 iterations).
====================================================================================================
LOWER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
| 1 | 227.981 | 227.981 | 95.2208 | 58.23302 | 0.87s
UPDATED DISC-VAR COUNT = 12 : [110, 111, 112, 113, 114, 115, 116, 117, 118, 122, 123, 124]
| finish | 262.7784 | 227.981 | 115.6223 | 49.28423 | 2.62s
====================================================================================================
*** Alpine ended with status UserLimits ***
┌ Warning: Not solved to optimality, status: UserLimits
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/nlp.jl:1283
Test Summary: | Pass Total
Partitioning variable selection tests :: castro6m2 | 14 14
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Warning: -/+Inf bounds detected on at least 1 variable. Initializing with values -/+10000.0. This may affect global optimality and run times.
PROBLEM STATISTICS
# of constraints = 45
# of non-linear constraints = 12
# of linear constraints = 33
# of continuous variables = 42
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 12
# of variables involved in nonlinear terms = 10
# of potential variables for partitioning = 10
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 1
Relative optimality gap criteria = 0.0100%
Potential variables chosen for partitioning = All
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.14s (0 iterations).
====================================================================================================
LOWER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
| finish | 470.3175 | 470.3175 | 78.0 | 83.41546 | 0.16s
====================================================================================================
*** Alpine ended with status UserLimits ***
┌ Warning: Not solved to optimality, status: UserLimits
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/nlp.jl:1283
Test Summary: | Pass Total
Test getsolvetime for time tracking | 1 1
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 9
# of non-linear constraints = 8
# of linear constraints = 1
# of continuous variables = 6
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 8
# of variables involved in nonlinear terms = 4
# of potential variables for partitioning = 4
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = true
Presolve maximum iterations = 10
=======================================================================
Test Summary: | Pass Total
Expression Parsing || bilinear || Affine || exprs.jl | 72 72
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 1
# of non-linear constraints = 1
# of linear constraints = 0
# of continuous variables = 2
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 3
# of variables involved in nonlinear terms = 2
# of potential variables for partitioning = 2
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = true
Presolve maximum iterations = 10
=======================================================================
Test Summary: | Pass Total
Expression Parsing || bilinear || Affine || nlp1.jl | 8 8
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 6
# of non-linear constraints = 3
# of linear constraints = 3
# of continuous variables = 8
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 5
# of variables involved in nonlinear terms = 8
# of potential variables for partitioning = 8
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = true
Presolve maximum iterations = 10
=======================================================================
Test Summary: | Pass Total
Expression Parsing || bilinear || Affine || nlp3.jl | 48 48
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 5
# of non-linear constraints = 5
# of linear constraints = 0
# of continuous variables = 6
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 8
# of variables involved in nonlinear terms = 4
# of potential variables for partitioning = 4
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = true
Presolve maximum iterations = 10
=======================================================================
Test Summary: | Pass Total
Expression Parsing || bilinear || Simple || bi1.jl | 7 7
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 213
# of non-linear constraints = 12
# of linear constraints = 201
# of continuous variables = 66
# of binary variables = 36
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 28
# of variables involved in nonlinear terms = 26
# of potential variables for partitioning = 10
=======================================================================
SUB-SOLVERS USED BY ALPINE
MINLP local solver = Pavito
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = true
Presolve maximum iterations = 10
=======================================================================
Test Summary: | Pass Total
Expression Parsing || bilinear || Complex || blend029.jl | 86 86
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 1
# of non-linear constraints = 0
# of linear constraints = 1
# of continuous variables = 3
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 1
# of variables involved in nonlinear terms = 3
# of potential variables for partitioning = 3
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = true
Presolve maximum iterations = 10
=======================================================================
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 1
# of non-linear constraints = 0
# of linear constraints = 1
# of continuous variables = 3
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 2
# of variables involved in nonlinear terms = 4
# of potential variables for partitioning = 4
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = true
Presolve maximum iterations = 10
=======================================================================
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 1
# of non-linear constraints = 0
# of linear constraints = 1
# of continuous variables = 3
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 2
# of variables involved in nonlinear terms = 4
# of potential variables for partitioning = 4
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = true
Presolve maximum iterations = 10
=======================================================================
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 1
# of non-linear constraints = 0
# of linear constraints = 1
# of continuous variables = 4
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 1
# of variables involved in nonlinear terms = 4
# of potential variables for partitioning = 4
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = true
Presolve maximum iterations = 10
=======================================================================
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 1
# of non-linear constraints = 0
# of linear constraints = 1
# of continuous variables = 4
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 3
# of variables involved in nonlinear terms = 6
# of potential variables for partitioning = 6
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = true
Presolve maximum iterations = 10
=======================================================================
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 1
# of non-linear constraints = 0
# of linear constraints = 1
# of continuous variables = 4
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 2
# of variables involved in nonlinear terms = 5
# of potential variables for partitioning = 5
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = true
Presolve maximum iterations = 10
=======================================================================
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 1
# of non-linear constraints = 0
# of linear constraints = 1
# of continuous variables = 4
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 2
# of variables involved in nonlinear terms = 5
# of potential variables for partitioning = 5
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = true
Presolve maximum iterations = 10
=======================================================================
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 1
# of non-linear constraints = 0
# of linear constraints = 1
# of continuous variables = 4
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 3
# of variables involved in nonlinear terms = 6
# of potential variables for partitioning = 6
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = true
Presolve maximum iterations = 10
=======================================================================
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 1
# of non-linear constraints = 0
# of linear constraints = 1
# of continuous variables = 4
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 2
# of variables involved in nonlinear terms = 5
# of potential variables for partitioning = 5
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = true
Presolve maximum iterations = 10
=======================================================================
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 1
# of non-linear constraints = 0
# of linear constraints = 1
# of continuous variables = 4
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 3
# of variables involved in nonlinear terms = 6
# of potential variables for partitioning = 6
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = true
Presolve maximum iterations = 10
=======================================================================
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 1
# of non-linear constraints = 0
# of linear constraints = 1
# of continuous variables = 4
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 2
# of variables involved in nonlinear terms = 5
# of potential variables for partitioning = 5
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = true
Presolve maximum iterations = 10
=======================================================================
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 1
# of non-linear constraints = 0
# of linear constraints = 1
# of continuous variables = 4
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 2
# of variables involved in nonlinear terms = 5
# of potential variables for partitioning = 5
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = true
Presolve maximum iterations = 10
=======================================================================
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 1
# of non-linear constraints = 0
# of linear constraints = 1
# of continuous variables = 4
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 3
# of variables involved in nonlinear terms = 6
# of potential variables for partitioning = 6
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = true
Presolve maximum iterations = 10
=======================================================================
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 1
# of non-linear constraints = 0
# of linear constraints = 1
# of continuous variables = 4
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 3
# of variables involved in nonlinear terms = 6
# of potential variables for partitioning = 6
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = true
Presolve maximum iterations = 10
=======================================================================
Test Summary: | Pass Total
Expression Parsing || multilinear || Simple || multi.jl | 197 197
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
┌ Warning: Special expression structure detected [*, coef, :call, ...]. Currently handling using a beta fix...
└ @ Alpine ~/.julia/packages/Alpine/yThaY/src/nlexpr.jl:329
┌ Warning: Special expression structure detected [*, coef, :call, ...]. Currently handling using a beta fix...
└ @ Alpine ~/.julia/packages/Alpine/yThaY/src/nlexpr.jl:329
┌ Warning: Special expression structure detected [*, coef, :call, ...]. Currently handling using a beta fix...
└ @ Alpine ~/.julia/packages/Alpine/yThaY/src/nlexpr.jl:329
┌ Warning: Special expression structure detected [*, coef, :call, ...]. Currently handling using a beta fix...
└ @ Alpine ~/.julia/packages/Alpine/yThaY/src/nlexpr.jl:329
┌ Warning: Special expression structure detected [*, coef, :call, ...]. Currently handling using a beta fix...
└ @ Alpine ~/.julia/packages/Alpine/yThaY/src/nlexpr.jl:329
┌ Warning: Special expression structure detected [*, coef, :call, ...]. Currently handling using a beta fix...
└ @ Alpine ~/.julia/packages/Alpine/yThaY/src/nlexpr.jl:329
Initial constraint-based bound evaluation exhausted...
┌ Warning: [INFEASIBLE] Infeasibility detected via bound propagation
└ @ Alpine ~/.julia/packages/Alpine/yThaY/src/bounds.jl:236
PROBLEM STATISTICS
# of constraints = 11
# of non-linear constraints = 5
# of linear constraints = 6
# of continuous variables = 2
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 3
# of variables involved in nonlinear terms = 2
# of potential variables for partitioning = 2
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = true
Presolve maximum iterations = 10
=======================================================================
Test Summary: | Pass Total
Expression Parsing || bilinear || Complex-div || div.jl | 65 65
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 5
# of non-linear constraints = 5
# of linear constraints = 0
# of continuous variables = 4
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 9
# of variables involved in nonlinear terms = 8
# of potential variables for partitioning = 8
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = true
Presolve maximum iterations = 10
=======================================================================
Test Summary: | Pass Total
Expression Parsing || part1 | 23 23
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 6
# of non-linear constraints = 6
# of linear constraints = 0
# of continuous variables = 4
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 15
# of variables involved in nonlinear terms = 12
# of potential variables for partitioning = 12
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = true
Presolve maximum iterations = 10
=======================================================================
Test Summary: | Pass Total
Expression Parsing || part2 | 36 36
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 6
# of non-linear constraints = 6
# of linear constraints = 0
# of continuous variables = 4
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 19
# of variables involved in nonlinear terms = 17
# of potential variables for partitioning = 7
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = true
Presolve maximum iterations = 10
=======================================================================
Test Summary: | Pass Total
Expression Parsing || part3 | 44 44
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 7
# of non-linear constraints = 7
# of linear constraints = 0
# of continuous variables = 4
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 11
# of variables involved in nonlinear terms = 8
# of potential variables for partitioning = 8
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = true
Presolve maximum iterations = 10
=======================================================================
Test Summary: | Pass Total
Expression Parsing || part7 | 29 29
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 9
# of non-linear constraints = 9
# of linear constraints = 0
# of continuous variables = 4
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 20
# of variables involved in nonlinear terms = 15
# of potential variables for partitioning = 15
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = true
Presolve maximum iterations = 10
=======================================================================
Test Summary: | Pass Total
Expression Parsing || part8 | 29 29
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
CONVEX Objective: 1.0 * x[1] * x[1] + 1.0 * x[3] * x[3]
CONVEX Constraint 1: 3.0 * x[1] * x[1] + 4.0 * x[2] * x[2] + -25.0 <= 0
CONVEX Constraint 2: 3.0 * x[1] * x[1] + 4.0 * x[2] * x[2] + -25.0 <= 0
CONVEX Constraint 4: 3.0 * x[1] * x[1] + 4.0 * x[2] * x[2] + -10.0 <= 0
CONVEX Constraint 5: 3.0 * x[1] * x[1] + 4.0 * x[2] * x[2] + 6.0 * x[3] * x[3] + -10.0 <= 0
CONVEX Constraint 6: (3.0 * x[1] ^ 0.5 + 4.0 * x[2] ^ 0.5 + 5.0 * x[5] ^ 0.5) - 100.0 <= 0.0
CONVEX Constraint 7: (-3.0 * x[1] ^ 0.5 - 4.0 * x[2] ^ 0.5) - -100.0 >= 0.0
CONVEX Constraint 8: (3.0 * x[1] ^ 3.0 + x[2] ^ 3.0 + 5.0 * x[3] ^ 3.0) - 200.0 <= 0.0
CONVEX Constraint 9: (1.0 * x[1] * x[1] * x[1] + 1.0 * x[2] * x[2] * x[2] + 1.0 * x[3] * x[3] * x[3] + 100.0 * x[4] * x[4] * x[4]) - 200.0 <= 0.0
CONVEX Constraint 10: (3.0 * x[1] * x[1] + 4.0 * x[2] * x[2]) - 25.0 <= 0.0
CONVEX Constraint 11: (3.0 * x[1] * x[1] + 4.0 * x[2] * x[2]) - 25.0 <= 0.0
CONVEX Constraint 12: ((3.0 * x[1] * x[1] + 4.0 * x[2] * x[2]) - 25.0) - 0.0 <= 0.0
CONVEX Constraint 13: (-3.0 * x[1] * x[1] - 4.0 * x[2] * x[2]) - -25.0 >= 0.0
CONVEX Constraint 14: (3.0 * x[1] * x[1] + 1.0 * x[2] * (5.0 * x[2])) - 25.0 <= 0.0
CONVEX Constraint 15: (3.0 * x[1] * x[1] + 5.0 * x[2] * x[2] + x[4] ^ 2.0) - 25.0 <= 0.0
CONVEX Constraint 16: (4.0 * x[1] ^ 2.0 + 5.0 * x[2] ^ 2.0) - 25.0 <= 0.0
CONVEX Constraint 19: (3.0 * x[1] * x[1] + 16.0 * x[2] ^ 2.0) - 40.0 <= 0.0
CONVEX Constraint 22: (3.0 * x[1] * x[1] + 4.0 * x[2] * x[2] + 5.0 * x[3] * x[3] + 6.0 * x[4] * x[4]) - 15.0 <= 0.0
CONVEX Constraint 25: (x[1] ^ 2.0 + x[2] ^ 2.0 + x[3] ^ 2.0 + x[4] ^ 2.0 + x[5] ^ 2.0) - 99999.0 <= 0.0
CONVEX Constraint 26: (3.0 * x[1] ^ 4.0 + 4.0 * x[2] ^ 4.0) - 200.0 <= 0.0
CONVEX Constraint 27: ((3.0 * x[1] ^ 4.0 + 4.0 * x[2] * x[2] * x[2] * x[2]) - 200.0) - 0.0 <= 0.0
CONVEX Constraint 28: (3.0 * x[1] ^ 4.0 + 1.0 * x[2] * x[2] * (4.0 * x[2] ^ 2.0)) - 200.0 <= 0.0
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 30
# of non-linear constraints = 30
# of linear constraints = 0
# of continuous variables = 5
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 21
# of detected nonlinear terms = 10
# of variables involved in nonlinear terms = 3
# of potential variables for partitioning = 3
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = true
Presolve maximum iterations = 10
=======================================================================
Test Summary: | Pass Total
Expression Parsing || Convex | 161 161
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 0
# of non-linear constraints = 0
# of linear constraints = 0
# of continuous variables = 2
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 4
# of variables involved in nonlinear terms = 4
# of potential variables for partitioning = 4
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 8
Bound-tightening presolve = true
Presolve maximum iterations = 10
=======================================================================
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 5
# of non-linear constraints = 5
# of linear constraints = 0
# of continuous variables = 3
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 9
# of variables involved in nonlinear terms = 9
# of potential variables for partitioning = 9
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = true
Presolve maximum iterations = 10
=======================================================================
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
Warning: -/+Inf bounds detected on at least 6907 variables. Initializing with values -/+10000.0. This may affect global optimality and run times.
Automatically turning OFF ratio branching due to the size of the problem
PROBLEM STATISTICS
# of constraints = 0
# of non-linear constraints = 0
# of linear constraints = 0
# of continuous variables = 6907
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 52
# of variables involved in nonlinear terms = 53
# of potential variables for partitioning = 27
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 8
Bound-tightening presolve = true
Presolve maximum iterations = 10
=======================================================================
Test Summary: | Pass Total
Expression Prasing || Linear Lifting | 638 638
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 80
# of non-linear constraints = 80
# of linear constraints = 0
# of continuous variables = 4
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 105
# of variables involved in nonlinear terms = 40
# of potential variables for partitioning = 11
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = true
Presolve maximum iterations = 10
=======================================================================
Test Summary: | Pass Total
Expression Parsing || Basic Multiplication Operators (Machine Generated for diffs) | 820 820
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 1
# of non-linear constraints = ┌ Warning: [INFEASIBLE] Infeasibility detected via bound propagation
└ @ Alpine ~/.julia/packages/Alpine/yThaY/src/bounds.jl:236
1
# of linear constraints = 0
# of continuous variables = 0
# of binary variables = 5
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 0
# of variables involved in nonlinear terms = 0
# of potential variables for partitioning = 0
=======================================================================
SUB-SOLVERS USED BY ALPINE
MINLP local solver = Pavito
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = true
Presolve maximum iterations = 10
=======================================================================
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
┌ Warning: Special expression structure detected [*, coef, :call, ...]. Currently handling using a beta fix...
└ @ Alpine ~/.julia/packages/Alpine/yThaY/src/nlexpr.jl:329
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 2
# of non-linear constraints = 1
# of linear constraints = 1
# of continuous variables = 5
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 0
# of variables involved in nonlinear terms = 0
# of potential variables for partitioning = 0
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = true
Presolve maximum iterations = 10
=======================================================================
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
┌ Warning: Special expression structure detected [*, coef, :call, ...]. Currently handling using a beta fix...
└ @ Alpine ~/.julia/packages/Alpine/yThaY/src/nlexpr.jl:329
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 2
# of non-linear constraints = 1
# of linear constraints = 1
# of continuous variables = 5
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 0
# of variables involved in nonlinear terms = 0
# of potential variables for partitioning = 0
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = true
Presolve maximum iterations = 10
=======================================================================
Test Summary: | Pass Total
Expression Parsing || corner cases | 25 25
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 8
# of non-linear constraints = 8
# of linear constraints = 0
# of continuous variables = 5
# of binary variables = 5
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 12
# of variables involved in nonlinear terms = 5
# of potential variables for partitioning = 5
=======================================================================
SUB-SOLVERS USED BY ALPINE
MINLP local solver = Pavito
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = true
Presolve maximum iterations = 10
=======================================================================
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 3
# of non-linear constraints = 3
# of linear constraints = 0
# of continuous variables = 5
# of binary variables = 5
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 12
# of variables involved in nonlinear terms = 6
# of potential variables for partitioning = 6
=======================================================================
SUB-SOLVERS USED BY ALPINE
MINLP local solver = Pavito
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = true
Presolve maximum iterations = 10
=======================================================================
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
┌ Warning: Alpine's support for integer variables is experimental
└ @ Alpine ~/.julia/packages/Alpine/yThaY/src/solver.jl:723
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 4
# of non-linear constraints = 4
# of linear constraints = 0
# of continuous variables = 0
# of binary variables = 0
# of integer variables = 10
# of detected convex constraints = 0
# of detected nonlinear terms = 6
# of variables involved in nonlinear terms = 16
# of potential variables for partitioning = 6
=======================================================================
SUB-SOLVERS USED BY ALPINE
MINLP local solver = Pavito
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = true
Presolve maximum iterations = 10
=======================================================================
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
┌ Warning: Alpine's support for integer variables is experimental
└ @ Alpine ~/.julia/packages/Alpine/yThaY/src/solver.jl:723
Initial constraint-based bound evaluation exhausted...
Warning: -/+Inf bounds detected on at least 1 variable. Initializing with values -/+10000.0. This may affect global optimality and run times.
PROBLEM STATISTICS
# of constraints = 35
# of non-linear constraints = 13
# of linear constraints = 22
# of continuous variables = 16
# of binary variables = 3
# of integer variables = 6
# of detected convex constraints = 0
# of detected nonlinear terms = 33
# of variables involved in nonlinear terms = 30
# of potential variables for partitioning = 15
=======================================================================
SUB-SOLVERS USED BY ALPINE
MINLP local solver = Pavito
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = true
Presolve maximum iterations = 10
=======================================================================
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...┌ Warning: Alpine's support for integer variables is experimental
└ @ Alpine ~/.julia/packages/Alpine/yThaY/src/solver.jl:723
PROBLEM STATISTICS
# of constraints = 2
# of non-linear constraints = 1
# of linear constraints = 1
# of continuous variables = 1
# of binary variables = 0
# of integer variables = 2
# of detected convex constraints = 0
# of detected nonlinear terms = 1
# of variables involved in nonlinear terms = 2
# of potential variables for partitioning = 2
=======================================================================
SUB-SOLVERS USED BY ALPINE
MINLP local solver = Pavito
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = true
Presolve maximum iterations = 10
=======================================================================
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
Warning: -/+Inf bounds detected on at least 1 variable. Initializing with values -/+10000.0. This may affect global optimality and run times.
PROBLEM STATISTICS
# of constraints = 14
# of non-linear constraints = 1
# of linear constraints = 13
# of continuous variables = 6
# of binary variables = 2
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 2
# of variables involved in nonlinear terms = 2
# of potential variables for partitioning = 2
=======================================================================
SUB-SOLVERS USED BY ALPINE
MINLP local solver = Pavito
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = true
Presolve maximum iterations = 10
=======================================================================
┌ Warning: Alpine's support for integer variables is experimental
└ @ Alpine ~/.julia/packages/Alpine/yThaY/src/solver.jl:723
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 21
# of non-linear constraints = 19
# of linear constraints = 2
# of continuous variables = 5
# of binary variables = 5
# of integer variables = 5
# of detected convex constraints = 0
# of detected nonlinear terms = 39
# of variables involved in nonlinear terms = 18
# of potential variables for partitioning = 9
=======================================================================
SUB-SOLVERS USED BY ALPINE
MINLP local solver = Pavito
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = true
Presolve maximum iterations = 10
=======================================================================
Test Summary: | Pass Total
Expression Parsing || Discrete Multilinear | 999 999
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 1
# of non-linear constraints = 1
# of linear constraints = 0
# of continuous variables = 2
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 3
# of variables involved in nonlinear terms = 2
# of potential variables for partitioning = 2
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Pavito
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.03s (0 iterations).
====================================================================================================
LOWER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
| 1 | 58.3837 | 58.3837 | 25.7092 | 55.96514 | 4.38s
| 2 | 58.3837 | 58.3837 | 51.5131 | 11.76791 | 5.45s
| 3 | 58.3837 | 58.3837 | 56.5857 | 3.07962 | 7.83s
| 4 | 58.3837 | 58.3837 | 58.2394 | 0.24718 | 11.56s
| 5 | 58.3837 | 58.3837 | 58.3641 | 0.03347 | 17.61s
| 6 | 58.3837 | 58.3837 | 58.3682 | 0.02654 | 28.56s
| finish | 58.3837 | 58.3837 | 58.3835 | 0.00036 | 45.99s
====================================================================================================
*** Alpine ended with status Optimal ***
Test Summary: | Pass Total
Validation Test || AMP-TMC || basic solve || exampls/nlp1.jl | 3 3
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 6
# of non-linear constraints = 3
# of linear constraints = 3
# of continuous variables = 8
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 5
# of variables involved in nonlinear terms = 8
# of potential variables for partitioning = 8
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 3
Relative optimality gap criteria = 0.0100%
Potential variables chosen for partitioning = All
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.02s (0 iterations).
====================================================================================================
LOWER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
| 1 | 7049.2479 | 7049.2479 | 3183.6748 | 54.83667 | 0.25s
| 2 | - | 7049.2479 | 5105.0043 | 27.58087 | 4.9s
| finish | 7062.6136 | 7049.2479 | 6485.8829 | 7.99184 | 23.38s
====================================================================================================
*** Alpine ended with status UserLimits ***
┌ Warning: Not solved to optimality, status: UserLimits
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/nlp.jl:1283
Test Summary: | Pass Total
Validation Test || AMP-TMC || basic solve || examples/nlp3.jl (3 iterations) | 3 3
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 6
# of non-linear constraints = 3
# of linear constraints = 3
# of continuous variables = 8
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 5
# of variables involved in nonlinear terms = 8
# of potential variables for partitioning = 3
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 3
Relative optimality gap criteria = 0.0100%
Potential variables chosen for partitioning = Min. vertex cover
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.07s (0 iterations).
====================================================================================================
LOWER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
| 1 | 7049.2479 | 7049.2479 | 2717.1317 | 61.45501 | 0.1s
| 2 | - | 7049.2479 | 2858.0668 | 59.45572 | 0.23s
| finish | - | 7049.2479 | 3647.178 | 48.26146 | 0.83s
====================================================================================================
*** Alpine ended with status UserLimits ***
Test Summary: | Pass Total
Validation Test || AMP-TMC || minimum-vertex solving || examples/nlp3.jl (3 iterations) | 4 4
┌ Warning: Not solved to optimality, status: UserLimits
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/nlp.jl:1283
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 1
# of non-linear constraints = 1
# of linear constraints = 0
# of continuous variables = 2
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 3
# of variables involved in nonlinear terms = 2
# of potential variables for partitioning = 2
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Pavito
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = true
Presolve maximum iterations = 10
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Starting bound-tightening
+ VAR 1 LB contracted 1.7000000000000002=>1.7000000000000002
+ VAR 1 UB contracted 3.3000000000000003=>3.3000000000000003
+ VAR 2 LB contracted 1.9000000000000001=>1.9000000000000001
+ VAR 2 UB contracted 3.7=>3.7
+ VAR 1 LB contracted 2.3000000000000003=>2.3000000000000003
+ VAR 1 UB contracted 2.8000000000000003=>2.8000000000000003
+ VAR 2 LB contracted 2.9000000000000004=>2.9000000000000004
+ VAR 2 UB contracted 3.4000000000000004=>3.4000000000000004
+ VAR 1 LB contracted 2.5=>2.5
+ VAR 1 UB contracted 2.7=>2.7
+ VAR 2 LB contracted 3.0=>3.0
+ VAR 2 UB contracted 3.2=>3.2
+ VAR 1 LB contracted 2.5=>2.5
+ VAR 1 UB contracted 2.6=>2.6
+ VAR 2 LB contracted 3.1=>3.1
+ VAR 2 UB contracted 3.2=>3.2
+ VAR 1 LB contracted 2.5=>2.5
+ VAR 1 UB contracted 2.6=>2.6
+ VAR 2 LB contracted 3.1=>3.1
+ VAR 2 UB contracted 3.2=>3.2
+ VAR 1 LB contracted 2.5=>2.5
+ VAR 1 UB contracted 2.6=>2.6
+ VAR 2 LB contracted 3.1=>3.1
+ VAR 2 UB contracted 3.2=>3.2
+ VAR 1 LB contracted 2.5=>2.5
+ VAR 1 UB contracted 2.6=>2.6
+ VAR 2 LB contracted 3.1=>3.1
+ VAR 2 UB contracted 3.2=>3.2
+ VAR 1 LB contracted 2.5=>2.5
+ VAR 1 UB contracted 2.6=>2.6
+ VAR 2 LB contracted 3.1=>3.1
+ VAR 2 UB contracted 3.2=>3.2
+ VAR 1 LB contracted 2.5=>2.5
+ VAR 1 UB contracted 2.6=>2.6
+ VAR 2 LB contracted 3.1=>3.1
+ VAR 2 UB contracted 3.2=>3.2
+ VAR 1 LB contracted 2.5=>2.5
+ VAR 1 UB contracted 2.6=>2.6
+ VAR 2 LB contracted 3.1=>3.1
+ VAR 2 UB contracted 3.2=>3.2
Variables whose bounds were tightened:
VAR 1: 99.0% contraction |1.0 --> | 2.5 - 2.6 | <-- 10.0 |
VAR 2: 99.0% contraction |1.0 --> | 3.1 - 3.2 | <-- 10.0 |
Completed presolve in 8.71s (10 iterations).
====================================================================================================
LOWER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
| 1 | 58.3837 | 58.3837 | 58.3775 | 0.0105 | 9.38s
| finish | 58.3837 | 58.3837 | 58.3821 | 0.00265 | 11.79s
====================================================================================================
*** Alpine ended with status Optimal ***
Test Summary: | Pass Total
Validation Test || PBT-AMP-TMC || basic solve || exampls/nlp1.jl | 3 3
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 6
# of non-linear constraints = 3
# of linear constraints = 3
# of continuous variables = 8
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 5
# of variables involved in nonlinear terms = 8
# of potential variables for partitioning = 8
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 3
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = true
Presolve maximum iterations = 2
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Starting bound-tightening
+ VAR 1 LB contracted 100.0=>100.0
+ VAR 1 UB contracted 4762.1=>4762.1
+ VAR 2 LB contracted 1000.0=>1000.0
+ VAR 2 UB contracted 5944.1=>5944.1
+ VAR 3 LB contracted 999.9000000000001=>999.9000000000001
+ VAR 3 UB contracted 5949.3=>5949.3
+ VAR 4 LB contracted 10.0=>10.0
+ VAR 4 UB contracted 334.20000000000005=>334.20000000000005
+ VAR 5 LB contracted 94.5=>94.5
+ VAR 5 UB contracted 572.8000000000001=>572.8000000000001
+ VAR 6 LB contracted 10.0=>10.0
+ VAR 6 UB contracted 390.1=>390.1
+ VAR 7 LB contracted 16.900000000000002=>16.900000000000002
+ VAR 7 UB contracted 626.9000000000001=>626.9000000000001
+ VAR 8 LB contracted 91.9=>91.9
+ VAR 8 UB contracted 660.7=>660.7
+ VAR 1 LB contracted 100.0=>100.0
+ VAR 1 UB contracted 4573.7=>4573.7
+ VAR 2 LB contracted 1000.0=>1000.0
+ VAR 2 UB contracted 5547.900000000001=>5547.900000000001
+ VAR 3 LB contracted 999.9000000000001=>999.9000000000001
+ VAR 3 UB contracted 5913.3=>5913.3
+ VAR 4 LB contracted 10.0=>10.0
+ VAR 4 UB contracted 332.40000000000003=>332.40000000000003
+ VAR 5 LB contracted 150.20000000000002=>150.20000000000002
+ VAR 5 UB contracted 551.0=>551.0
+ VAR 6 LB contracted 10.0=>10.0
+ VAR 6 UB contracted 390.0=>390.0
+ VAR 7 LB contracted 35.4=>35.4
+ VAR 7 UB contracted 571.1=>571.1
+ VAR 8 LB contracted 168.0=>168.0
+ VAR 8 UB contracted 638.7=>638.7
Variables whose bounds were tightened:
VAR 7: 30.0% contraction |10.0 --> | 35.4 - 571.1 | <-- 780.0 |
VAR 4: 15.0% contraction |10.0 --> | 10.0 - 332.4 | <-- 390.0 |
VAR 2: 49.0% contraction |1000.0 --> | 1000.0 - 5547.9 | <-- 10000.0 |
VAR 3: 45.0% contraction |1000.0 --> | 999.9 - 5913.3 | <-- 10000.0 |
VAR 8: 46.0% contraction |10.0 --> | 168.0 - 638.7 | <-- 880.0 |
VAR 5: 48.0% contraction |10.0 --> | 150.2 - 551.0 | <-- 780.0 |
VAR 1: 55.00000000000001% contraction |100.0 --> | 100.0 - 4573.7 | <-- 10000.0 |
Completed presolve in 0.16s (2 iterations).
====================================================================================================
LOWER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
| 1 | 7049.2479 | 7049.2479 | 3741.1268 | 46.92871 | 0.49s
| 2 | - | 7049.2479 | 6158.3329 | 12.63844 | 20.61s
| finish | 7049.2479 | 7049.2479 | 6853.9044 | 2.77113 | 63.99s
====================================================================================================
*** Alpine ended with status UserLimits ***
┌ Warning: Not solved to optimality, status: UserLimits
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/nlp.jl:1283
Test Summary: | Pass Total
Validation Test || BT-AMP-TMC || basic solve || examples/nlp3.jl | 3 3
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 6
# of non-linear constraints = 3
# of linear constraints = 3
# of continuous variables = 8
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 5
# of variables involved in nonlinear terms = 8
# of potential variables for partitioning = 8
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Pavito
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 2
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = true
Presolve maximum iterations = 2
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Starting bound-tightening
+ VAR 1 LB contracted 99.9=>99.9
+ VAR 1 UB contracted 4408.6=>4408.6
+ VAR 2 LB contracted 999.9000000000001=>999.9000000000001
+ VAR 2 UB contracted 5586.0=>5586.0
+ VAR 3 LB contracted 1162.9=>1162.9
+ VAR 3 UB contracted 5949.5=>5949.5
+ VAR 4 LB contracted 9.9=>9.9
+ VAR 4 UB contracted 267.6=>267.6
+ VAR 5 LB contracted 154.8=>154.8
+ VAR 5 UB contracted 426.8=>426.8
+ VAR 6 LB contracted 9.9=>9.9
+ VAR 6 UB contracted 390.0=>390.0
+ VAR 7 LB contracted 64.5=>64.5
+ VAR 7 UB contracted 482.3=>482.3
+ VAR 8 LB contracted 254.8=>254.8
+ VAR 8 UB contracted 526.8000000000001=>526.8000000000001
+ VAR 1 LB contracted 99.80000000000001=>99.80000000000001
+ VAR 1 UB contracted 2507.4=>2507.4
+ VAR 2 LB contracted 999.8000000000001=>999.8000000000001
+ VAR 2 UB contracted 4060.6000000000004=>4060.6000000000004
+ VAR 3 LB contracted 2994.7000000000003=>2994.7000000000003
+ VAR 3 UB contracted 5830.6=>5830.6
+ VAR 4 LB contracted 20.1=>20.1
+ VAR 4 UB contracted 245.8=>245.8
+ VAR 5 LB contracted 260.1=>260.1
+ VAR 5 UB contracted 371.3=>371.3
+ VAR 6 LB contracted 9.8=>9.8
+ VAR 6 UB contracted 375.20000000000005=>375.20000000000005
+ VAR 7 LB contracted 150.20000000000002=>150.20000000000002
+ VAR 7 UB contracted 363.90000000000003=>363.90000000000003
+ VAR 8 LB contracted 360.1=>360.1
+ VAR 8 UB contracted 471.3=>471.3
Variables whose bounds were tightened:
VAR 7: 72.0% contraction |10.0 --> | 150.2 - 363.9 | <-- 780.0 |
VAR 4: 41.0% contraction |10.0 --> | 20.1 - 245.8 | <-- 390.0 |
VAR 2: 66.0% contraction |1000.0 --> | 999.8 - 4060.6 | <-- 10000.0 |
VAR 3: 68.0% contraction |1000.0 --> | 2994.7 - 5830.6 | <-- 10000.0 |
VAR 8: 87.0% contraction |10.0 --> | 360.1 - 471.3 | <-- 880.0 |
VAR 5: 86.0% contraction |10.0 --> | 260.1 - 371.3 | <-- 780.0 |
VAR 6: 4.0% contraction |10.0 --> | 9.8 - 375.2 | <-- 390.0 |
VAR 1: 76.0% contraction |100.0 --> | 99.8 - 2507.4 | <-- 10000.0 |
Completed presolve in 21.57s (2 iterations).
====================================================================================================
LOWER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
| 1 | 7049.2479 | 7049.2479 | 6119.4905 | 13.18946 | 22.76s
| finish | 7271.9996 | 7049.2479 | 6653.21 | 5.61816 | 48.33s
====================================================================================================
*** Alpine ended with status UserLimits ***
Test Summary: | Pass Total
Validation Test || PBT-AMP-TMC || basic solve || examples/nlp3.jl | 3 3
┌ Warning: Not solved to optimality, status: UserLimits
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/nlp.jl:1283
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 1
# of non-linear constraints = 1
# of linear constraints = 0
# of continuous variables = 2
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 3
# of variables involved in nonlinear terms = 2
# of potential variables for partitioning = 2
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Pavito
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.01s (0 iterations).
====================================================================================================
LOWER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
| 1 | 58.3837 | 58.3837 | 25.7092 | 55.96514 | 1.42s
| 2 | 58.3837 | 58.3837 | 51.5132 | 11.76786 | 2.01s
| 3 | 58.3837 | 58.3837 | 56.5857 | 3.07957 | 3.11s
| 4 | 58.3837 | 58.3837 | 58.2394 | 0.24709 | 4.68s
| 5 | 58.3837 | 58.3837 | 58.3642 | 0.03335 | 7.55s
| 6 | 58.3837 | 58.3837 | 58.3683 | 0.02637 | 12.41s
| finish | 58.3837 | 58.3837 | 58.3831 | 0.00091 | 18.9s
====================================================================================================
*** Alpine ended with status Optimal ***
Test Summary: | Pass Total
Validation Test || AMP-CONV || basic solve || examples/nlp1.jl | 3 3
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 1
# of non-linear constraints = 1
# of linear constraints = 0
# of continuous variables = 2
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 3
# of variables involved in nonlinear terms = 2
# of potential variables for partitioning = 2
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Pavito
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = true
Presolve maximum iterations = 10
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Starting bound-tightening
+ VAR 1 LB contracted 1.7695=>1.7695
+ VAR 1 UB contracted 3.2273300000000003=>3.2273300000000003
+ VAR 2 LB contracted 1.9931100000000002=>1.9931100000000002
+ VAR 2 UB contracted 3.63723=>3.63723
+ VAR 1 LB contracted 2.4067800000000004=>2.4067800000000004
+ VAR 1 UB contracted 2.69632=>2.69632
+ VAR 2 LB contracted 2.9480000000000004=>2.9480000000000004
+ VAR 2 UB contracted 3.3025800000000003=>3.3025800000000003
+ VAR 1 LB contracted 2.5256100000000004=>2.5256100000000004
+ VAR 1 UB contracted 2.58558=>2.58558
+ VAR 2 LB contracted 3.09323=>3.09323
+ VAR 2 UB contracted 3.1666800000000004=>3.1666800000000004
+ VAR 1 LB contracted 2.5495500000000004=>2.5495500000000004
+ VAR 1 UB contracted 2.56198=>2.56198
+ VAR 2 LB contracted 3.1225500000000004=>3.1225500000000004
+ VAR 2 UB contracted 3.13777=>3.13777
+ VAR 1 LB contracted 2.5544700000000002=>2.5544700000000002
+ VAR 1 UB contracted 2.5570700000000004=>2.5570700000000004
+ VAR 2 LB contracted 3.1285700000000003=>3.1285700000000003
+ VAR 2 UB contracted 3.1317700000000004=>3.1317700000000004
+ VAR 1 LB contracted 2.55527=>2.55527
+ VAR 1 UB contracted 2.5562700000000005=>2.5562700000000005
+ VAR 2 LB contracted 3.12967=>3.12967
+ VAR 2 UB contracted 3.1306700000000003=>3.1306700000000003
!┌ Warning: mixed-integer cycling detected, terminating Pavito
└ @ Pavito ~/.julia/packages/Pavito/rjnTF/src/algorithm.jl:273
┌ Warning: mixed-integer cycling detected, terminating Pavito
└ @ Pavito ~/.julia/packages/Pavito/rjnTF/src/algorithm.jl:273
!+ VAR 1 LB contracted 2.55527=>2.55527
+ VAR 1 UB contracted 2.5562700000000005=>2.5562700000000005
┌ Warning: mixed-integer cycling detected, terminating Pavito
└ @ Pavito ~/.julia/packages/Pavito/rjnTF/src/algorithm.jl:273
!!+ VAR 2 LB contracted 3.12967=>3.12967
+ VAR 2 UB contracted 3.1306700000000003=>3.1306700000000003
!┌ Warning: mixed-integer cycling detected, terminating Pavito
└ @ Pavito ~/.julia/packages/Pavito/rjnTF/src/algorithm.jl:273
┌ Warning: mixed-integer cycling detected, terminating Pavito
└ @ Pavito ~/.julia/packages/Pavito/rjnTF/src/algorithm.jl:273
!+ VAR 1 LB contracted 2.55527=>2.55527
+ VAR 1 UB contracted 2.5562700000000005=>2.5562700000000005
┌ Warning: mixed-integer cycling detected, terminating Pavito
└ @ Pavito ~/.julia/packages/Pavito/rjnTF/src/algorithm.jl:273
!!+ VAR 2 LB contracted 3.12967=>3.12967
+ VAR 2 UB contracted 3.1306700000000003=>3.1306700000000003
┌ Warning: mixed-integer cycling detected, terminating Pavito
└ @ Pavito ~/.julia/packages/Pavito/rjnTF/src/algorithm.jl:273
!┌ Warning: mixed-integer cycling detected, terminating Pavito
└ @ Pavito ~/.julia/packages/Pavito/rjnTF/src/algorithm.jl:273
!+ VAR 1 LB contracted 2.55527=>2.55527
+ VAR 1 UB contracted 2.5562700000000005=>2.5562700000000005
┌ Warning: mixed-integer cycling detected, terminating Pavito
└ @ Pavito ~/.julia/packages/Pavito/rjnTF/src/algorithm.jl:273
!!+ VAR 2 LB contracted 3.12967=>3.12967
+ VAR 2 UB contracted 3.1306700000000003=>3.1306700000000003
┌ Warning: mixed-integer cycling detected, terminating Pavito
└ @ Pavito ~/.julia/packages/Pavito/rjnTF/src/algorithm.jl:273
!┌ Warning: mixed-integer cycling detected, terminating Pavito
└ @ Pavito ~/.julia/packages/Pavito/rjnTF/src/algorithm.jl:273
!+ VAR 1 LB contracted 2.55527=>2.55527
+ VAR 1 UB contracted 2.5562700000000005=>2.5562700000000005
┌ Warning: mixed-integer cycling detected, terminating Pavito
└ @ Pavito ~/.julia/packages/Pavito/rjnTF/src/algorithm.jl:273
!!+ VAR 2 LB contracted 3.12967=>3.12967
+ VAR 2 UB contracted 3.1306700000000003=>3.1306700000000003
Variables whose bounds were tightened:
VAR 1: 100.0% contraction |1.0 --> | 2.5553 - 2.5563 | <-- 10.0 |
VAR 2: 100.0% contraction |1.0 --> | 3.1297 - 3.1307 | <-- 10.0 |
Completed presolve in 3.27s (10 iterations).
====================================================================================================
LOWER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
| finish | 58.3837 | 58.3837 | 58.3837 | 0.0 | 3.41s
====================================================================================================
*** Alpine ended with status Optimal ***
Test Summary: | Pass Total
Validation Test || PBT-AMP-CONV || basic solve || examples/nlp1.jl | 3 3
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
PROBLEM STATISTICS
# of constraints = 6
# of non-linear constraints = 3
# of linear constraints = 3
# of continuous variables = 8
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 5
# of variables involved in nonlinear terms = 8
# of potential variables for partitioning = 8
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.02s (0 iterations).
====================================================================================================
LOWER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
| 1 | 7049.2479 | 7049.2479 | 3004.247 | 57.38202 | 0.11s
| 2 | - | 7049.2479 | 4896.6075 | 30.53716 | 0.51s
| 3 | 7049.2479 | 7049.2479 | 5871.5307 | 16.70699 | 1.4s
| 4 | - | 7049.2479 | 6717.2923 | 4.70909 | 8.63s
| 5 | 7049.267 | 7049.2479 | 6901.8131 | 2.0915 | 17.77s
| 6 | 7049.9758 | 7049.2479 | 7020.723 | 0.40465 | 35.92s
| 7 | 7050.6454 | 7049.2479 | 7042.4094 | 0.09701 | 111.07s
| 8 | 7050.7924 | 7049.2479 | 7047.8904 | 0.01926 | 196.89s
| finish | 7049.2479 | 7049.2479 | 7048.8389 | 0.0058 | 349.75s
====================================================================================================
*** Alpine ended with status Optimal ***
Test Summary: | Pass Total
Validation Test || AMP-CONV || basic solve || examples/nlp3.jl | 3 3
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 1
# of non-linear constraints = 1
# of linear constraints = 0
# of continuous variables = 2
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 2
# of variables involved in nonlinear terms = 2
# of potential variables for partitioning = 2
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Pavito
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 6
Relative optimality gap criteria = 0.0100%
Discretization ratio = 8
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.03s (0 iterations).
====================================================================================================
LOWER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
| 1 | 2.0 | 2.0 | 1.3846 | 30.76921 | 0.2s
| 2 | 1.4142 | 1.4142 | 1.3846 | 2.0929 | 0.33s
| 3 | 1.4142 | 1.4142 | 1.4114 | 0.202 | 0.6s
| 4 | 1.4142 | 1.4142 | 1.4114 | 0.20197 | 1.03s
| 5 | 1.4142 | 1.4142 | 1.414 | 0.01284 | 1.36s
| finish | 1.4142 | 1.4142 | 1.414 | 0.0126 | 1.86s
====================================================================================================
*** Alpine ended with status UserLimits ***
Test Summary: | Pass Total
Validation Test || AMP || basic solve || examples/circle.jl | 1 1
┌ Warning: Not solved to optimality, status: UserLimits
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/nlp.jl:1283
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 1
# of non-linear constraints = 1
# of linear constraints = 0
# of continuous variables = 4
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 4
# of variables involved in nonlinear terms = 4
# of potential variables for partitioning = 4
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Pavito
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 8
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.01s (0 iterations).
====================================================================================================
LOWER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
| 1 | 4.0 | 4.0 | 1.8182 | 54.54542 | 0.27s
| 2 | 2.0 | 2.0 | 1.8182 | 9.09063 | 0.35s
┌ Warning: mixed-integer cycling detected, terminating Pavito
└ @ Pavito ~/.julia/packages/Pavito/rjnTF/src/algorithm.jl:273
| 3 | 2.0 | 2.0 | 1.8182 | 9.09063 | 1.4s
| 4 | 2.0 | 2.0 | 1.982 | 0.89984 | 1.76s
┌ Warning: mixed-integer cycling detected, terminating Pavito
└ @ Pavito ~/.julia/packages/Pavito/rjnTF/src/algorithm.jl:273
┌ Warning: mixed-integer cycling detected, terminating Pavito
└ @ Pavito ~/.julia/packages/Pavito/rjnTF/src/algorithm.jl:273
| 5 | 2.0 | 2.0 | 1.9967 | 0.16707 | 2.23s
┌ Warning: mixed-integer cycling detected, terminating Pavito
└ @ Pavito ~/.julia/packages/Pavito/rjnTF/src/algorithm.jl:273
| 6 | 2.0 | 2.0 | 1.9985 | 0.07431 | 2.46s
| 7 | 2.0 | 2.0 | 1.9998 | 0.01054 | 2.87s
┌ Warning: mixed-integer cycling detected, terminating Pavito
└ @ Pavito ~/.julia/packages/Pavito/rjnTF/src/algorithm.jl:273
┌ Warning: mixed-integer cycling detected, terminating Pavito
└ @ Pavito ~/.julia/packages/Pavito/rjnTF/src/algorithm.jl:273
| finish | 2.0 | 2.0 | 1.9999 | 0.00483 | 3.76s
====================================================================================================
*** Alpine ended with status Optimal ***
Test Summary: | Pass Total
Validation Test || AMP || basic solve || examples/circleN.jl | 1 1
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 1
# of non-linear constraints = 1
# of linear constraints = 0
# of continuous variables = 2
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 3
# of variables involved in nonlinear terms = 2
# of potential variables for partitioning = 2
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Pavito
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.01s (0 iterations).
====================================================================================================
LOWER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
| 1 | 58.3837 | 58.3837 | 25.7092 | 55.96514 | 0.07s
| 2 | 58.3837 | 58.3837 | 51.5132 | 11.76787 | 0.64s
| 3 | 58.3837 | 58.3837 | 56.5857 | 3.07965 | 2.09s
| 4 | 58.3837 | 58.3837 | 58.2394 | 0.24719 | 4.38s
| 5 | 58.3837 | 58.3837 | 58.3641 | 0.03346 | 8.35s
| 6 | 58.3837 | 58.3837 | 58.3683 | 0.02641 | 16.46s
| finish | 58.3837 | 58.3837 | 58.3835 | 0.00027 | 28.37s
====================================================================================================
*** Alpine ended with status Optimal ***
Test Summary: | Pass Total
Validation Test || AMP-CONV-FACET || basic solve || examples/nlp1.jl | 3 3
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 1
# of non-linear constraints = 1
# of linear constraints = 0
# of continuous variables = 2
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 3
# of variables involved in nonlinear terms = 2
# of potential variables for partitioning = 2
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Pavito
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.01s (0 iterations).
====================================================================================================
LOWER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
| 1 | 58.3837 | 58.3837 | 25.7092 | 55.96514 | 0.07s
| 2 | 58.3837 | 58.3837 | 51.5132 | 11.76788 | 0.68s
| 3 | 58.3837 | 58.3837 | 56.5857 | 3.0796 | 1.81s
| 4 | 58.3837 | 58.3837 | 58.2394 | 0.24713 | 3.69s
| 5 | 58.3837 | 58.3837 | 58.3641 | 0.03346 | 6.94s
| 6 | 58.3837 | 58.3837 | 58.3682 | 0.02651 | 13.35s
| finish | 58.3837 | 58.3837 | 58.3835 | 0.00028 | 21.01s
====================================================================================================
*** Alpine ended with status Optimal ***
Test Summary: | Pass Total
Validation Test || AMP-CONV-MINIB || basic solve || examples/nlp1.jl | 3 3
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
PROBLEM STATISTICS
# of constraints = 2
# of non-linear constraints = 0
# of linear constraints = 2
# of continuous variables = 7
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 2
# of variables involved in nonlinear terms = 7
# of potential variables for partitioning = 7
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 4
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.14s (0 iterations).
====================================================================================================
UPPER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Upper Bound | Gap (%) | Time
| 1 | 2.0 | 2.0 | 1198.6577 | LARGE | 2.2s
| 2 | 2.0 | 2.0 | 89.1649 | LARGE | 2.59s
| 3 | 1.9996 | 2.0 | 11.7873 | 489.36322 | 4.17s
| finish | 1.9981 | 2.0 | 4.6806 | 134.02933 | 11.26s
====================================================================================================
*** Alpine ended with status UserLimits ***
┌ Warning: Not solved to optimality, status: UserLimits
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/nlp.jl:1283
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
PROBLEM STATISTICS
# of constraints = 2
# of non-linear constraints = 0
# of linear constraints = 2
# of continuous variables = 7
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 6
# of variables involved in nonlinear terms = 11
# of potential variables for partitioning = 11
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 4
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.01s (0 iterations).
====================================================================================================
UPPER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Upper Bound | Gap (%) | Time
| 1 | 2.0 | 2.0 | 3384.3337 | LARGE | 0.04s
| 2 | 2.0 | 2.0 | 325.129 | LARGE | 0.12s
| 3 | 2.0 | 2.0 | 51.5116 | LARGE | 1.87s
| finish | 2.0 | 2.0 | 12.0917 | 504.58388 | 6.56s
====================================================================================================
*** Alpine ended with status UserLimits ***
┌ Warning: Not solved to optimality, status: UserLimits
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/nlp.jl:1283
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
PROBLEM STATISTICS
# of constraints = 2
# of non-linear constraints = 0
# of linear constraints = 2
# of continuous variables = 7
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 4
# of variables involved in nonlinear terms = 9
# of potential variables for partitioning = 9
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 4
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.01s (0 iterations).
====================================================================================================
UPPER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Upper Bound | Gap (%) | Time
| 1 | 2.0 | 2.0 | 1839.9969 | LARGE | 2.13s
| 2 | 2.0 | 2.0 | 134.6944 | LARGE | 2.42s
| 3 | 2.0 | 2.0 | 16.956 | 747.79944 | 3.65s
| finish | 1.8386 | 2.0 | 8.946 | 347.30196 | 6.58s
====================================================================================================
*** Alpine ended with status UserLimits ***
┌ Warning: Not solved to optimality, status: UserLimits
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/nlp.jl:1283
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
PROBLEM STATISTICS
# of constraints = 2
# of non-linear constraints = 0
# of linear constraints = 2
# of continuous variables = 7
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 4
# of variables involved in nonlinear terms = 9
# of potential variables for partitioning = 9
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 4
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.01s (0 iterations).
====================================================================================================
UPPER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Upper Bound | Gap (%) | Time
| 1 | 2.0 | 2.0 | 1683.4018 | LARGE | 0.03s
| 2 | 2.0 | 2.0 | 135.2151 | LARGE | 0.33s
| 3 | 1.5612 | 2.0 | 22.6612 | LARGE | 1.57s
┌ Warning: Not solved to optimality, status: UserLimits
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/nlp.jl:1283
| finish | 0.9637 | 2.0 | 10.0278 | 401.38759 | 5.19s
====================================================================================================
*** Alpine ended with status UserLimits ***
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
PROBLEM STATISTICS
# of constraints = 2
# of non-linear constraints = 0
# of linear constraints = 2
# of continuous variables = 7
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 6
# of variables involved in nonlinear terms = 11
# of potential variables for partitioning = 11
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 4
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.01s (0 iterations).
====================================================================================================
UPPER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Upper Bound | Gap (%) | Time
| 1 | 2.0 | 2.0 | 2015.988 | LARGE | 0.03s
| 2 | 2.0 | 2.0 | 138.8799 | LARGE | 0.1s
| 3 | 2.0 | 2.0 | 22.5156 | LARGE | 1.15s
| finish | 1.9954 | 2.0 | 8.1001 | 305.00284 | 5.38s
====================================================================================================
*** Alpine ended with status UserLimits ***
┌ Warning: Not solved to optimality, status: UserLimits
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/nlp.jl:1283
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
PROBLEM STATISTICS
# of constraints = 2
# of non-linear constraints = 0
# of linear constraints = 2
# of continuous variables = 7
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 4
# of variables involved in nonlinear terms = 9
# of potential variables for partitioning = 9
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 4
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.01s (0 iterations).
====================================================================================================
UPPER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Upper Bound | Gap (%) | Time
| 1 | 2.0 | 2.0 | 1525.3528 | LARGE | 0.03s
| 2 | 2.0 | 2.0 | 107.6271 | LARGE | 0.18s
| 3 | 2.0 | 2.0 | 19.9841 | 899.20601 | 1.7s
| finish | 1.9942 | 2.0 | 6.6384 | 231.91992 | 7.06s
====================================================================================================
*** Alpine ended with status UserLimits ***
┌ Warning: Not solved to optimality, status: UserLimits
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/nlp.jl:1283
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
PROBLEM STATISTICS
# of constraints = 2
# of non-linear constraints = 0
# of linear constraints = 2
# of continuous variables = 7
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 6
# of variables involved in nonlinear terms = 11
# of potential variables for partitioning = 11
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 4
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.01s (0 iterations).
====================================================================================================
UPPER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Upper Bound | Gap (%) | Time
| 1 | 2.0 | 2.0 | 1694.9434 | LARGE | 0.03s
| 2 | 2.0 | 2.0 | 132.8052 | LARGE | 0.25s
| 3 | 0.4787 | 2.0 | 22.0495 | LARGE | 1.28s
| finish | 1.1929 | 2.0 | 12.5674 | 528.37079 | 4.05s
====================================================================================================
*** Alpine ended with status UserLimits ***
┌ Warning: Not solved to optimality, status: UserLimits
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/nlp.jl:1283
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
PROBLEM STATISTICS
# of constraints = 2
# of non-linear constraints = 0
# of linear constraints = 2
# of continuous variables = 7
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 4
# of variables involved in nonlinear terms = 9
# of potential variables for partitioning = 9
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 4
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.01s (0 iterations).
====================================================================================================
UPPER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Upper Bound | Gap (%) | Time
| 1 | 2.0 | 2.0 | 1539.6071 | LARGE | 0.03s
| 2 | 2.0 | 2.0 | 125.3375 | LARGE | 0.21s
| 3 | 2.0 | 2.0 | 26.8211 | LARGE | 0.96s
| finish | 1.7576 | 2.0 | 7.3975 | 269.87351 | 3.41s
====================================================================================================
*** Alpine ended with status UserLimits ***
┌ Warning: Not solved to optimality, status: UserLimits
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/nlp.jl:1283
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
PROBLEM STATISTICS
# of constraints = 2
# of non-linear constraints = 0
# of linear constraints = 2
# of continuous variables = 7
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 4
# of variables involved in nonlinear terms = 9
# of potential variables for partitioning = 9
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 4
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.01s (0 iterations).
====================================================================================================
UPPER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Upper Bound | Gap (%) | Time
| 1 | 2.0 | 2.0 | 1231.5048 | LARGE | 0.04s
| 2 | 2.0 | 2.0 | 91.4042 | LARGE | 0.23s
| 3 | 1.3953 | 2.0 | 17.1876 | 759.38064 | 1.4s
| finish | 1.6677 | 2.0 | 6.0293 | 201.46374 | 5.05s
====================================================================================================
*** Alpine ended with status UserLimits ***
┌ Warning: Not solved to optimality, status: UserLimits
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/nlp.jl:1283
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
PROBLEM STATISTICS
# of constraints = 2
# of non-linear constraints = 0
# of linear constraints = 2
# of continuous variables = 7
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 6
# of variables involved in nonlinear terms = 11
# of potential variables for partitioning = 11
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 4
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.02s (0 iterations).
====================================================================================================
UPPER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Upper Bound | Gap (%) | Time
| 1 | 2.0 | 2.0 | 1552.9434 | LARGE | 0.05s
| 2 | 2.0 | 2.0 | 127.7429 | LARGE | 0.16s
| 3 | 1.4894 | 2.0 | 31.2166 | LARGE | 0.87s
┌ Warning: Not solved to optimality, status: UserLimits
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/nlp.jl:1283
| finish | 1.8099 | 2.0 | 7.9147 | 295.73342 | 3.45s
====================================================================================================
*** Alpine ended with status UserLimits ***
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
PROBLEM STATISTICS
# of constraints = 2
# of non-linear constraints = 0
# of linear constraints = 2
# of continuous variables = 7
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 6
# of variables involved in nonlinear terms = 11
# of potential variables for partitioning = 11
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 4
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.01s (0 iterations).
====================================================================================================
UPPER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Upper Bound | Gap (%) | Time
| 1 | 2.0 | 2.0 | 1634.9476 | LARGE | 0.04s
| 2 | 2.0 | 2.0 | 129.2433 | LARGE | 0.13s
| 3 | 2.0 | 2.0 | 31.0681 | LARGE | 0.94s
| finish | 1.8676 | 2.0 | 7.8831 | 294.15314 | 3.55s
====================================================================================================
*** Alpine ended with status UserLimits ***
┌ Warning: Not solved to optimality, status: UserLimits
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/nlp.jl:1283
Test Summary: | Pass Total
Validation Test || AMP || multi4N || N = 2 || exprmode=1:11 | 22 22
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
PROBLEM STATISTICS
# of constraints = 1
# of non-linear constraints = 0
# of linear constraints = 1
# of continuous variables = 2
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 1
# of variables involved in nonlinear terms = 2
# of potential variables for partitioning = 2
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 4
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.01s (0 iterations).
====================================================================================================
UPPER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Upper Bound | Gap (%) | Time
| 1 | 1.0 | 1.0 | 1.4754 | 47.53999 | 0.03s
| 2 | 1.0 | 1.0 | 1.1189 | 11.885 | 0.35s
| 3 | 1.0 | 1.0 | 1.0297 | 2.97125 | 0.76s
| finish | 1.0 | 1.0 | 1.0074 | 0.74281 | 1.33s
====================================================================================================
*** Alpine ended with status UserLimits ***
┌ Warning: Not solved to optimality, status: UserLimits
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/nlp.jl:1283
Test Summary: | Pass Total
Validation Test || AMP || multi2 || exprmode=1:11 | 3 3
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
PROBLEM STATISTICS
# of constraints = 2
# of non-linear constraints = 0
# of linear constraints = 2
# of continuous variables = 5
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 2
# of variables involved in nonlinear terms = 5
# of potential variables for partitioning = 5
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 4
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.01s (0 iterations).
====================================================================================================
UPPER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Upper Bound | Gap (%) | Time
| 1 | 2.0 | 2.0 | 115.5111 | LARGE | 0.02s
| 2 | 2.0 | 2.0 | 19.5971 | 879.85682 | 0.24s
| 3 | 2.0 | 2.0 | 5.6617 | 183.0836 | 0.98s
┌ Warning: Not solved to optimality, status: UserLimits
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/nlp.jl:1283
| finish | 2.0 | 2.0 | 2.9719 | 48.59304 | 4.14s
====================================================================================================
*** Alpine ended with status UserLimits ***
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
PROBLEM STATISTICS
# of constraints = 2
# of non-linear constraints = 0
# of linear constraints = 2
# of continuous variables = 5
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 4
# of variables involved in nonlinear terms = 7
# of potential variables for partitioning = 7
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 4
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.01s (0 iterations).
====================================================================================================
UPPER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Upper Bound | Gap (%) | Time
| 1 | 2.0 | 2.0 | 170.4212 | LARGE | 0.03s
| 2 | 2.0 | 2.0 | 31.781 | LARGE | 0.07s
| 3 | 2.0 | 2.0 | 8.1634 | 308.16738 | 1.0s
┌ Warning: Not solved to optimality, status: UserLimits
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/nlp.jl:1283
| finish | 1.683 | 2.0 | 3.8549 | 92.74608 | 2.72s
====================================================================================================
*** Alpine ended with status UserLimits ***
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
PROBLEM STATISTICS
# of constraints = 2
# of non-linear constraints = 0
# of linear constraints = 2
# of continuous variables = 5
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 4
# of variables involved in nonlinear terms = 7
# of potential variables for partitioning = 7
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 4
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.01s (0 iterations).
====================================================================================================
UPPER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Upper Bound | Gap (%) | Time
| 1 | 2.0 | 2.0 | 116.1203 | LARGE | 0.02s
| 2 | 2.0 | 2.0 | 20.1217 | 906.08259 | 0.24s
| 3 | 2.0 | 2.0 | 6.2298 | 211.48986 | 0.75s
| finish | 1.327 | 2.0 | 4.2337 | 111.68729 | 1.89s
====================================================================================================
*** Alpine ended with status UserLimits ***
┌ Warning: Not solved to optimality, status: UserLimits
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/nlp.jl:1283
Test Summary: | Pass Total
Validation Test || AMP || multi3N || N = 2 || exprmode=1:11 | 9 9
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
PROBLEM STATISTICS
# of constraints = 3
# of non-linear constraints = 0
# of linear constraints = 3
# of continuous variables = 9
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 3
# of variables involved in nonlinear terms = 9
# of potential variables for partitioning = 9
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 3
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.01s (0 iterations).
====================================================================================================
UPPER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Upper Bound | Gap (%) | Time
| 1 | 3.0 | 3.0 | 493.9224 | LARGE | 0.02s
| 2 | 3.0 | 3.0 | 52.1469 | LARGE | 0.04s
┌ Warning: Not solved to optimality, status: UserLimits
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/nlp.jl:1283
| finish | 3.0 | 3.0 | 12.0546 | 301.82003 | 0.11s
====================================================================================================
*** Alpine ended with status UserLimits ***
Test Summary: | Pass Total
Validation Test || AMP || multiKND || K = 3, N = 3, D = 0 | 3 3
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
PROBLEM STATISTICS
# of constraints = 6
# of non-linear constraints = 3
# of linear constraints = 3
# of continuous variables = 8
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 5
# of variables involved in nonlinear terms = 8
# of potential variables for partitioning = 8
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 4
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.02s (0 iterations).
====================================================================================================
LOWER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
| 1 | 7049.2479 | 7049.2479 | 3004.247 | 57.38202 | 0.06s
| 2 | - | 7049.2479 | 4896.6075 | 30.53716 | 0.63s
| 3 | 7049.2479 | 7049.2479 | 5871.5307 | 16.70699 | 2.09s
| finish | - | 7049.2479 | 6717.2923 | 4.70909 | 13.63s
====================================================================================================
*** Alpine ended with status UserLimits ***
┌ Warning: Not solved to optimality, status: UserLimits
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/nlp.jl:1283
Test Summary: | Pass Total
Validation Test || AMP-CONV-FACET || basic solve || examples/nlp3.jl | 5 5
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
PROBLEM STATISTICS
# of constraints = 6
# of non-linear constraints = 3
# of linear constraints = 3
# of continuous variables = 8
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 5
# of variables involved in nonlinear terms = 8
# of potential variables for partitioning = 8
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Pavito
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 4
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.02s (0 iterations).
====================================================================================================
LOWER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
| 1 | 7049.2479 | 7049.2479 | 3004.2447 | 57.38205 | 0.37s
| 2 | - | 7049.2479 | 4896.6023 | 30.53724 | 1.59s
| 3 | 7049.2479 | 7049.2479 | 5871.5043 | 16.70737 | 3.84s
| finish | - | 7049.2479 | 6717.2663 | 4.70946 | 13.18s
====================================================================================================
*** Alpine ended with status UserLimits ***
┌ Warning: Not solved to optimality, status: UserLimits
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/nlp.jl:1283
Test Summary: | Pass Total
Validation Test || AMP-CONV-MINIB || basic solve || examples/nlp3.jl | 5 5
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 6
# of non-linear constraints = 3
# of linear constraints = 3
# of continuous variables = 8
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 5
# of variables involved in nonlinear terms = 8
# of potential variables for partitioning = 8
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 1
Relative optimality gap criteria = 0.0100%
Discretization ratio = 18
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.02s (0 iterations).
====================================================================================================
LOWER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
| finish | 7049.2479 | 7049.2479 | 4564.5646 | 35.24749 | 0.19s
====================================================================================================
*** Alpine ended with status UserLimits ***
┌ Warning: Not solved to optimality, status: UserLimits
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/nlp.jl:1283
Test Summary: | Pass Total
Validation Test || AMP || DISC-RATIO || examples/nlp3.jl | 2 2
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 6
# of non-linear constraints = 3
# of linear constraints = 3
# of continuous variables = 8
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 5
# of variables involved in nonlinear terms = 8
# of potential variables for partitioning = 8
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 1
Relative optimality gap criteria = 0.0100%
Discretization ratio branch activated
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
BRANCH RATIO = 8, METRIC = 3819.013761489449 || TIME = 0.22115182876586914
BRANCH RATIO = 10, METRIC = 4179.164527298521 || TIME = 0.20144104957580566
BRANCH RATIO = 12, METRIC = 4624.887358342879 || TIME = 0.23030591011047363
BRANCH RATIO = 14, METRIC = 4892.857796407751 || TIME = 0.20858287811279297
BRANCH RATIO = 16, METRIC = 4703.605750887826 || TIME = 0.1535348892211914
BRANCH RATIO = 18, METRIC = 4564.564633804461 || TIME = 0.15296697616577148
BRANCH RATIO = 20, METRIC = 4458.109939204688 || TIME = 0.16748285293579102
RATIO BRANCHING OFF due to solution variance test passed.
INCUMB_RATIO = 14
Completed presolve in 1.36s (0 iterations).
====================================================================================================
LOWER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
| finish | 7049.2479 | 7049.2479 | 4892.8578 | 30.59036 | 1.83s
====================================================================================================
*** Alpine ended with status UserLimits ***
Test Summary: | Pass Total
Validation Test || AMP || DISC-RATIO-BRANCH || examples/nlp3.jl | 2 2
┌ Warning: Not solved to optimality, status: UserLimits
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/nlp.jl:1283
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 45
# of non-linear constraints = 12
# of linear constraints = 33
# of continuous variables = 42
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 12
# of variables involved in nonlinear terms = 10
# of potential variables for partitioning = 10
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 1
Relative optimality gap criteria = 0.0100%
Discretization ratio branch activated
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
BRANCH RATIO = 8, METRIC = 126.3195920376292 || TIME = 0.2398509979248047
BRANCH RATIO = 10, METRIC = 117.60281326714141 || TIME = 0.18468403816223145
BRANCH RATIO = 12, METRIC = 117.80537562435867 || TIME = 0.32010984420776367
BRANCH RATIO = 14, METRIC = 117.94301405714619 || TIME = 0.1760849952697754
BRANCH RATIO = 16, METRIC = 118.04249900754138 || TIME = 0.1841568946838379
BRANCH RATIO = 18, METRIC = 118.1177044290736 || TIME = 0.13962984085083008
BRANCH RATIO = 20, METRIC = 111.34145454545464 || TIME = 0.14858102798461914
RATIO BRANCHING OFF due to solution variance test passed.
INCUMB_RATIO = 8
Completed presolve in 1.48s (0 iterations).
====================================================================================================
LOWER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
| finish | 470.3186 | 470.3186 | 126.6953 | 73.0618 | 2.01s
====================================================================================================
*** Alpine ended with status UserLimits ***
Test Summary: | Pass Total
Validation Test || AMP || DISC-RATIO-BRANCH || examples/castro2m2.jl | 2 2
┌ Warning: Not solved to optimality, status: UserLimits
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/nlp.jl:1283
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 3
# of non-linear constraints = 0
# of linear constraints = 3
# of continuous variables = 7
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 3
# of variables involved in nonlinear terms = 7
# of potential variables for partitioning = 7
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 1
Relative optimality gap criteria = 0.0100%
Discretization ratio branch activated
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
BRANCH RATIO = 8, METRIC = 4.025156249999999 || TIME = 2.6931591033935547
BRANCH RATIO = 10, METRIC = 3.6560999999999995 || TIME = 4.014413118362427
BRANCH RATIO = 12, METRIC = 3.4556249999999995 || TIME = 3.8180668354034424
BRANCH RATIO = 14, METRIC = 3.3347448979591836 || TIME = 4.75900411605835
BRANCH RATIO = 16, METRIC = 3.2562890624999996 || TIME = 3.778291940689087
BRANCH RATIO = 18, METRIC = 3.29975 || TIME = 3.6606898307800293
BRANCH RATIO = 20, METRIC = 3.456980192307692 || TIME = 3.2600290775299072
RATIO BRANCHING OFF due to solution variance test passed.
INCUMB_RATIO = 16
Completed presolve in 26.0s (0 iterations).
====================================================================================================
UPPER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Upper Bound | Gap (%) | Time
| finish | 3.0 | 3.0 | 3.1057 | 3.52458 | 45.13s
====================================================================================================
*** Alpine ended with status UserLimits ***
┌ Warning: Not solved to optimality, status: UserLimits
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/nlp.jl:1283
Test Summary: | Pass Total
Validation Test || AMP || DISC-RATIO-BRANCH || examples/multi3N.jl exprmode=2 | 2 2
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
PROBLEM STATISTICS
# of constraints = 3
# of non-linear constraints = 0
# of linear constraints = 3
# of continuous variables = 7
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 3
# of variables involved in nonlinear terms = 7
# of potential variables for partitioning = 7
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 1
Relative optimality gap criteria = 0.0100%
Discretization ratio branch activated
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
BRANCH RATIO = 8, METRIC = 123.18749490186853 || TIME = 0.034958839416503906
BRANCH RATIO = 10, METRIC = 84.10407284461928 || TIME = 0.030746936798095703
BRANCH RATIO = 12, METRIC = 62.407611028446134 || TIME = 0.2072610855102539
BRANCH RATIO = 14, METRIC = 48.98644568998813 || TIME = 0.179595947265625
BRANCH RATIO = 16, METRIC = 40.03366942787157 || TIME = 0.35660505294799805
BRANCH RATIO = 18, METRIC = 34.24655371522786 || TIME = 0.3486518859863281
BRANCH RATIO = 20, METRIC = 31.055147046529918 || TIME = 0.3991999626159668
RATIO BRANCHING OFF due to solution variance test passed.
INCUMB_RATIO = 20
Completed presolve in 1.57s (0 iterations).
====================================================================================================
UPPER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Upper Bound | Gap (%) | Time
| finish | 3.0 | 3.0 | 3.3028 | 10.0933 | 5.38s
====================================================================================================
*** Alpine ended with status UserLimits ***
┌ Warning: Not solved to optimality, status: UserLimits
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/nlp.jl:1283
Test Summary: | Pass Total
Validation Test || AMP || DISC-RATIO-BRANCH || examples/multi3N.jl exprmode=2 | 2 2
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 2
# of non-linear constraints = 0
# of linear constraints = 2
# of continuous variables = 7
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 2
# of variables involved in nonlinear terms = 7
# of potential variables for partitioning = 7
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 1
Relative optimality gap criteria = 0.0100%
Discretization ratio branch activated
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
BRANCH RATIO = 8, METRIC = 4.512012499999994 || TIME = 5.445003986358643
BRANCH RATIO = 10, METRIC = 3.588792319999988 || TIME = 7.6452720165252686
BRANCH RATIO = 12, METRIC = 3.096199999999998 || TIME = 11.990797996520996
BRANCH RATIO = 14, METRIC = 3.172791836734693 || TIME = 8.84130597114563
BRANCH RATIO = 16, METRIC = 3.547533928571432 || TIME = 11.711990118026733
BRANCH RATIO = 18, METRIC = 3.8557000000000006 || TIME = 9.009721994400024
BRANCH RATIO = 20, METRIC = 4.11284 || TIME = 8.380316972732544
RATIO BRANCHING OFF due to solution variance test passed.
INCUMB_RATIO = 12
Completed presolve in 63.04s (0 iterations).
====================================================================================================
UPPER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Upper Bound | Gap (%) | Time
| finish | 2.0 | 2.0 | 2.7103 | 35.51398 | 107.52s
====================================================================================================
*** Alpine ended with status UserLimits ***
┌ Warning: Not solved to optimality, status: UserLimits
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/nlp.jl:1283
Test Summary: | Pass Total
Validation Test || AMP || DISC-RATIO-BRANCH || examples/multi4N.jl exprmode=1 | 2 2
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
PROBLEM STATISTICS
# of constraints = 2
# of non-linear constraints = 0
# of linear constraints = 2
# of continuous variables = 7
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 2
# of variables involved in nonlinear terms = 7
# of potential variables for partitioning = 7
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 1
Relative optimality gap criteria = 0.0100%
Discretization ratio branch activated
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
BRANCH RATIO = 8, METRIC = 395.63282418091467 || TIME = 0.054631948471069336
BRANCH RATIO = 10, METRIC = 283.08886013160964 || TIME = 0.33122801780700684
BRANCH RATIO = 12, METRIC = 213.22674041148738 || TIME = 0.08983802795410156
BRANCH RATIO = 14, METRIC = 167.2880670713117 || TIME = 0.3376271724700928
BRANCH RATIO = 16, METRIC = 135.53972121628215 || TIME = 0.2671389579772949
BRANCH RATIO = 18, METRIC = 112.68170227280606 || TIME = 0.24120306968688965
BRANCH RATIO = 20, METRIC = 95.66165331346589 || TIME = 0.34169602394104004
RATIO BRANCHING OFF due to solution variance test passed.
INCUMB_RATIO = 20
Completed presolve in 1.68s (0 iterations).
====================================================================================================
UPPER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Upper Bound | Gap (%) | Time
| finish | 2.0 | 2.0 | 5.3222 | 166.10988 | 5.55s
====================================================================================================
*** Alpine ended with status UserLimits ***
┌ Warning: Not solved to optimality, status: UserLimits
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/nlp.jl:1283
Test Summary: | Pass Total
Validation Test || AMP || DISC-RATIO-BRANCH || examples/multi4N.jl exprmode=2 | 2 2
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 2
# of non-linear constraints = 0
# of linear constraints = 2
# of continuous variables = 7
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 6
# of variables involved in nonlinear terms = 11
# of potential variables for partitioning = 11
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 1
Relative optimality gap criteria = 0.0100%
Discretization ratio branch activated
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
BRANCH RATIO = 8, METRIC = 6.487399999999999 || TIME = 2.56323504447937
BRANCH RATIO = 10, METRIC = 5.325017600000001 || TIME = 1.9231269359588623
BRANCH RATIO = 12, METRIC = 6.0479692307692305 || TIME = 2.2124969959259033
BRANCH RATIO = 14, METRIC = 6.411638782045077 || TIME = 2.9624650478363037
BRANCH RATIO = 16, METRIC = 5.713379995207001 || TIME = 3.3362159729003906
BRANCH RATIO = 18, METRIC = 5.1977924663183925 || TIME = 3.4560670852661133
BRANCH RATIO = 20, METRIC = 4.803130895055459 || TIME = 3.770726203918457
RATIO BRANCHING OFF due to solution variance test passed.
INCUMB_RATIO = 20
Completed presolve in 20.24s (0 iterations).
====================================================================================================
UPPER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Upper Bound | Gap (%) | Time
| finish | 2.0 | 2.0 | 3.6234 | 81.16972 | 30.32s
====================================================================================================
*** Alpine ended with status UserLimits ***
┌ Warning: Not solved to optimality, status: UserLimits
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/nlp.jl:1283
Test Summary: | Pass Total
Validation Test || AMP || DISC-RATIO-BRANCH || examples/multi4N.jl exprmode=2 | 2 2
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 1
# of non-linear constraints = 0
# of linear constraints = 1
# of continuous variables = 5
# of binary variables = 5
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 5
# of variables involved in nonlinear terms = 0
# of potential variables for partitioning = 0
=======================================================================
SUB-SOLVERS USED BY ALPINE
MINLP local solver = Pavito
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.14s (0 iterations).
====================================================================================================
LOWER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
| finish | 0.3 | 0.3 | 0.3 | 1.0e-5 | 0.82s
====================================================================================================
*** Alpine ended with status Optimal ***
Test Summary: | Pass Total
Operator :: bmpl && binlin && binprod solve test I | 11 11
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 1
# of non-linear constraints = 0
# of linear constraints = 1
# of continuous variables = 5
# of binary variables = 5
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 10
# of variables involved in nonlinear terms = 5
# of potential variables for partitioning = 5
=======================================================================
SUB-SOLVERS USED BY ALPINE
MINLP local solver = Pavito
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
┌ Warning: Warning: VAR7 SOL=51.95118 out of discretization [50.0,51.951175182290065]. Taking middle point...
└ @ Alpine ~/.julia/packages/Alpine/yThaY/src/amp.jl:285
┌ Warning: Warning: VAR8 SOL=52.16891 out of discretization [50.0,52.16890548606979]. Taking middle point...
└ @ Alpine ~/.julia/packages/Alpine/yThaY/src/amp.jl:285
Completed presolve in 0.03s (0 iterations).
====================================================================================================
UPPER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Upper Bound | Gap (%) | Time
| finish | 15422.0584 | 15422.0584 | 15422.0581 | 0.0 | 0.04s
====================================================================================================
*** Alpine ended with status Optimal ***
Test Summary: | Pass Total
Operator :: bmpl && binlin && binprod solve test II | 21 21
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 1
# of non-linear constraints = 0
# of linear constraints = 1
# of continuous variables = 5
# of binary variables = 5
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 10
# of variables involved in nonlinear terms = 5
# of potential variables for partitioning = 5
=======================================================================
SUB-SOLVERS USED BY ALPINE
MINLP local solver = Pavito
MIP solver = Pavito
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = true
Presolve maximum iterations = 10
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Starting bound-tightening
!!+ VAR 1 LB contracted 0.0=>0.0
+ VAR 1 UB contracted 1.0=>1.0
!!+ VAR 2 LB contracted 0.0=>0.0
+ VAR 2 UB contracted 1.0=>1.0
!!+ VAR 3 LB contracted 0.0=>0.0
+ VAR 3 UB contracted 1.0=>1.0
!!+ VAR 4 LB contracted 0.0=>0.0
+ VAR 4 UB contracted 1.0=>1.0
!!+ VAR 5 LB contracted 0.0=>0.0
+ VAR 5 UB contracted 1.0=>1.0
!!+ VAR 6 LB contracted 50.0=>50.0
+ VAR 6 UB contracted 54.146569806491904=>54.146569806491904
!!+ VAR 7 LB contracted 50.0=>50.0
+ VAR 7 UB contracted 51.951175182290065=>51.951175182290065
!!+ VAR 8 LB contracted 50.0=>50.0
+ VAR 8 UB contracted 52.16890548606979=>52.16890548606979
!!+ VAR 9 LB contracted 50.0=>50.0
+ VAR 9 UB contracted 81.12170312869416=>81.12170312869416
!!+ VAR 10 LB contracted 50.0=>50.0
+ VAR 10 UB contracted 69.85570489311311=>69.85570489311311
Variables whose bounds were tightened:
┌ Warning: Warning: VAR7 SOL=51.95118 out of discretization [50.0,51.951175182290065]. Taking middle point...
└ @ Alpine ~/.julia/packages/Alpine/yThaY/src/amp.jl:285
┌ Warning: Warning: VAR8 SOL=52.16891 out of discretization [50.0,52.16890548606979]. Taking middle point...
└ @ Alpine ~/.julia/packages/Alpine/yThaY/src/amp.jl:285
┌ Warning: Warning: VAR7 SOL=51.95118 out of discretization [50.0,51.951175182290065]. Taking middle point...
└ @ Alpine ~/.julia/packages/Alpine/yThaY/src/amp.jl:285
┌ Warning: Warning: VAR8 SOL=52.16891 out of discretization [50.0,52.16890548606979]. Taking middle point...
└ @ Alpine ~/.julia/packages/Alpine/yThaY/src/amp.jl:285
Completed presolve in 1.71s (1 iterations).
====================================================================================================
UPPER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Upper Bound | Gap (%) | Time
| finish | 19812.9209 | 19812.9209 | 19812.9205 | 0.0 | 1.82s
====================================================================================================
*** Alpine ended with status Optimal ***
Test Summary: | Pass Total
Operator :: bmpl && binlin && binprod solve test II | 32 32
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 1
# of non-linear constraints = 1
# of linear constraints = 0
# of continuous variables = 2
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 3
# of variables involved in nonlinear terms = 2
# of potential variables for partitioning = 2
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Pavito
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Using convhull_ebd formulation
Encoding method = default
Independent branching scheme = false
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.01s (0 iterations).
====================================================================================================
LOWER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
| 1 | 58.3837 | 58.3837 | 25.7092 | 55.96514 | 0.06s
| 2 | 58.3837 | 58.3837 | 51.5131 | 11.768 | 1.78s
| 3 | 58.3837 | 58.3837 | 56.5857 | 3.07966 | 2.96s
| 4 | 58.3837 | 58.3837 | 58.2393 | 0.24721 | 4.18s
| 5 | 58.3837 | 58.3837 | 58.3641 | 0.03348 | 6.99s
| 6 | 58.3837 | 58.3837 | 58.3681 | 0.02661 | 10.76s
| finish | 58.3837 | 58.3837 | 58.3834 | 0.00043 | 15.7s
====================================================================================================
*** Alpine ended with status Optimal ***
Test Summary: | Pass Total
Embedding Test || AMP-CONV || basic solve || examples/nlp1.jl | 3 3
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 1
# of non-linear constraints = 1
# of linear constraints = 0
# of continuous variables = 2
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 3
# of variables involved in nonlinear terms = 2
# of potential variables for partitioning = 2
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Pavito
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Using convhull_ebd formulation
Encoding method = default
Independent branching scheme = false
Bound-tightening presolve = true
Presolve maximum iterations = 10
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Starting bound-tightening
+ VAR 1 LB contracted 1.7695=>1.7695
+ VAR 1 UB contracted 3.2273300000000003=>3.2273300000000003
+ VAR 2 LB contracted 1.9931100000000002=>1.9931100000000002
+ VAR 2 UB contracted 3.63723=>3.63723
+ VAR 1 LB contracted 2.4067800000000004=>2.4067800000000004
+ VAR 1 UB contracted 2.69632=>2.69632
+ VAR 2 LB contracted 2.9480000000000004=>2.9480000000000004
+ VAR 2 UB contracted 3.3025800000000003=>3.3025800000000003
+ VAR 1 LB contracted 2.5256100000000004=>2.5256100000000004
+ VAR 1 UB contracted 2.58558=>2.58558
+ VAR 2 LB contracted 3.09323=>3.09323
+ VAR 2 UB contracted 3.1666800000000004=>3.1666800000000004
+ VAR 1 LB contracted 2.5495500000000004=>2.5495500000000004
+ VAR 1 UB contracted 2.56198=>2.56198
+ VAR 2 LB contracted 3.1225500000000004=>3.1225500000000004
+ VAR 2 UB contracted 3.13777=>3.13777
+ VAR 1 LB contracted 2.5544700000000002=>2.5544700000000002
+ VAR 1 UB contracted 2.5570700000000004=>2.5570700000000004
+ VAR 2 LB contracted 3.1285700000000003=>3.1285700000000003
+ VAR 2 UB contracted 3.1317600000000003=>3.1317600000000003
+ VAR 1 LB contracted 2.55527=>2.55527
+ VAR 1 UB contracted 2.5562700000000005=>2.5562700000000005
+ VAR 2 LB contracted 3.12967=>3.12967
+ VAR 2 UB contracted 3.1306700000000003=>3.1306700000000003
┌ Warning: mixed-integer cycling detected, terminating Pavito
└ @ Pavito ~/.julia/packages/Pavito/rjnTF/src/algorithm.jl:273
!+ VAR 1 LB contracted 2.5552700000000006=>2.5552700000000006
+ VAR 1 UB contracted 2.5562700000000005=>2.5562700000000005
!+ VAR 2 LB contracted 3.1296700000000004=>3.1296700000000004
+ VAR 2 UB contracted 3.1306700000000003=>3.1306700000000003
+ VAR 1 LB contracted 2.5552700000000006=>2.5552700000000006
+ VAR 1 UB contracted 2.5562700000000005=>2.5562700000000005
+ VAR 2 LB contracted 3.1296700000000004=>3.1296700000000004
+ VAR 2 UB contracted 3.1306700000000003=>3.1306700000000003
+ VAR 1 LB contracted 2.5552700000000006=>2.5552700000000006
+ VAR 1 UB contracted 2.5562700000000005=>2.5562700000000005
+ VAR 2 LB contracted 3.1296700000000004=>3.1296700000000004
+ VAR 2 UB contracted 3.1306700000000003=>3.1306700000000003
+ VAR 1 LB contracted 2.5552700000000006=>2.5552700000000006
+ VAR 1 UB contracted 2.5562700000000005=>2.5562700000000005
+ VAR 2 LB contracted 3.1296700000000004=>3.1296700000000004
+ VAR 2 UB contracted 3.1306700000000003=>3.1306700000000003
Variables whose bounds were tightened:
VAR 1: 100.0% contraction |1.0 --> | 2.5553 - 2.5563 | <-- 10.0 |
VAR 2: 100.0% contraction |1.0 --> | 3.1297 - 3.1307 | <-- 10.0 |
Completed presolve in 2.2s (10 iterations).
====================================================================================================
LOWER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
┌ Warning: mixed-integer cycling detected, terminating Pavito
└ @ Pavito ~/.julia/packages/Pavito/rjnTF/src/algorithm.jl:273
| finish | 58.3837 | 58.3837 | 58.3837 | 0.0 | 2.3s
====================================================================================================
*** Alpine ended with status Optimal ***
Test Summary: | Pass Total
Embedding Test || PBT-AMP-CONV || basic solve || examples/nlp1.jl | 3 3
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
PROBLEM STATISTICS
# of constraints = 6
# of non-linear constraints = 3
# of linear constraints = 3
# of continuous variables = 8
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 5
# of variables involved in nonlinear terms = 8
# of potential variables for partitioning = 8
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Using convhull_ebd formulation
Encoding method = default
Independent branching scheme = false
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.03s (0 iterations).
====================================================================================================
LOWER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
| 1 | 7049.2479 | 7049.2479 | 3004.247 | 57.38202 | 0.09s
| 2 | - | 7049.2479 | 4896.6075 | 30.53716 | 0.41s
| 3 | 7049.2479 | 7049.2479 | 5871.5307 | 16.70699 | 1.32s
| 4 | - | 7049.2479 | 6717.2923 | 4.70909 | 4.33s
| 5 | 7049.267 | 7049.2479 | 6901.8131 | 2.0915 | 9.17s
| 6 | 7049.9758 | 7049.2479 | 7020.723 | 0.40465 | 16.34s
| 7 | 7050.6454 | 7049.2479 | 7042.4094 | 0.09701 | 30.93s
| 8 | 7050.7924 | 7049.2479 | 7047.8904 | 0.01926 | 53.34s
| finish | 7049.2479 | 7049.2479 | 7048.8389 | 0.0058 | 111.01s
====================================================================================================
*** Alpine ended with status Optimal ***
Test Summary: | Pass Total
Embedding Test || AMP-CONV || basic solve || examples/nlp3.jl | 3 3
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 1
# of non-linear constraints = 1
# of linear constraints = 0
# of continuous variables = 2
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 2
# of variables involved in nonlinear terms = 2
# of potential variables for partitioning = 2
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Pavito
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 6
Relative optimality gap criteria = 0.0100%
Discretization ratio = 8
Using convhull_ebd formulation
Encoding method = default
Independent branching scheme = false
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.02s (0 iterations).
====================================================================================================
LOWER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
| 1 | 2.0 | 2.0 | 1.3846 | 30.76922 | 0.07s
| 2 | 1.4142 | 1.4142 | 1.3846 | 2.09291 | 0.21s
| 3 | 1.4142 | 1.4142 | 1.3846 | 2.09291 | 0.36s
| 4 | 1.4142 | 1.4142 | 1.4114 | 0.20205 | 0.54s
| 5 | 1.4142 | 1.4142 | 1.414 | 0.01356 | 0.75s
| finish | 1.4142 | 1.4142 | 1.4141 | 0.00968 | 1.09s
====================================================================================================
*** Alpine ended with status Optimal ***
Test Summary: | Pass Total
Embedding Test || AMP || special problem || ... | 1 1
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 1
# of non-linear constraints = 1
# of linear constraints = 0
# of continuous variables = 2
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 3
# of variables involved in nonlinear terms = 2
# of potential variables for partitioning = 2
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Pavito
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Using convhull_ebd formulation
Encoding method = default
Independent branching scheme = true
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.01s (0 iterations).
====================================================================================================
LOWER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
| 1 | 58.3837 | 58.3837 | 25.7092 | 55.96514 | 0.06s
| 2 | 58.3837 | 58.3837 | 51.5131 | 11.768 | 0.61s
| 3 | 58.3837 | 58.3837 | 56.5857 | 3.07966 | 1.79s
| 4 | 58.3837 | 58.3837 | 58.2393 | 0.24731 | 3.12s
| 5 | 58.3837 | 58.3837 | 58.3641 | 0.03348 | 6.22s
| 6 | 58.3837 | 58.3837 | 58.3681 | 0.02661 | 10.2s
| finish | 58.3837 | 58.3837 | 58.3834 | 0.00055 | 15.1s
====================================================================================================
*** Alpine ended with status Optimal ***
Test Summary: | Pass Total
Embedding IBS Test || AMP-CONV || basic solve || examples/nlp1.jl | 3 3
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
PROBLEM STATISTICS
# of constraints = 6
# of non-linear constraints = 3
# of linear constraints = 3
# of continuous variables = 8
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 5
# of variables involved in nonlinear terms = 8
# of potential variables for partitioning = 8
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Using convhull_ebd formulation
Encoding method = default
Independent branching scheme = true
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.02s (0 iterations).
====================================================================================================
LOWER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
| 1 | 7049.2479 | 7049.2479 | 3004.247 | 57.38202 | 0.15s
| 2 | - | 7049.2479 | 4896.6075 | 30.53716 | 0.48s
| 3 | 7049.2479 | 7049.2479 | 5871.5307 | 16.70699 | 1.76s
| 4 | - | 7049.2479 | 6717.2923 | 4.70909 | 5.35s
| 5 | 7049.267 | 7049.2479 | 6901.8131 | 2.0915 | 10.09s
| 6 | 7049.9758 | 7049.2479 | 7020.723 | 0.40465 | 18.35s
| 7 | 7050.6454 | 7049.2479 | 7042.4094 | 0.09701 | 29.34s
| 8 | 7050.7924 | 7049.2479 | 7047.8904 | 0.01926 | 48.34s
| finish | 7049.2479 | 7049.2479 | 7048.8389 | 0.0058 | 79.31s
====================================================================================================
*** Alpine ended with status Optimal ***
Test Summary: | Pass Total
Embedding IBS Test || AMP-CONV || basic solve || examples/nlp3.jl | 3 3
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 1
# of non-linear constraints = 1
# of linear constraints = 0
# of continuous variables = 2
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 2
# of variables involved in nonlinear terms = 2
# of potential variables for partitioning = 2
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Pavito
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 6
Relative optimality gap criteria = 0.0100%
Discretization ratio = 8
Using convhull_ebd formulation
Encoding method = default
Independent branching scheme = true
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.02s (0 iterations).
====================================================================================================
LOWER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
| 1 | 2.0 | 2.0 | 1.3846 | 30.76922 | 0.09s
| 2 | 1.4142 | 1.4142 | 1.3846 | 2.09291 | 0.25s
| 3 | 1.4142 | 1.4142 | 1.3846 | 2.0929 | 0.34s
| 4 | 1.4142 | 1.4142 | 1.4114 | 0.20205 | 0.57s
| 5 | 1.4142 | 1.4142 | 1.414 | 0.01356 | 0.79s
| finish | 1.4142 | 1.4142 | 1.4141 | 0.00968 | 1.06s
====================================================================================================
*** Alpine ended with status Optimal ***
Test Summary: | Pass Total
Embedding IBS Test || AMP || special problem || ... | 1 1
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 1
# of non-linear constraints = 1
# of linear constraints = 0
# of continuous variables = 2
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 3
# of variables involved in nonlinear terms = 2
# of potential variables for partitioning = 2
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Pavito
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Using convhull_ebd formulation
Encoding method = default
Independent branching scheme = false
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.01s (0 iterations).
====================================================================================================
LOWER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
| 1 | 58.3837 | 58.3837 | 25.7092 | 55.96514 | 0.06s
| 2 | 58.3837 | 58.3837 | 51.5131 | 11.768 | 1.48s
| 3 | 58.3837 | 58.3837 | 56.5857 | 3.07966 | 3.11s
| 4 | 58.3837 | 58.3837 | 58.2393 | 0.24721 | 4.85s
| 5 | 58.3837 | 58.3837 | 58.3641 | 0.03348 | 8.49s
| 6 | 58.3837 | 58.3837 | 58.3681 | 0.02661 | 13.8s
| finish | 58.3837 | 58.3837 | 58.3834 | 0.00043 | 19.54s
====================================================================================================
*** Alpine ended with status Optimal ***
Test Summary: | Pass Total
Embedding LINK Test || AMP-CONV || basic solve || examples/nlp1.jl | 3 3
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
PROBLEM STATISTICS
# of constraints = 6
# of non-linear constraints = 3
# of linear constraints = 3
# of continuous variables = 8
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 5
# of variables involved in nonlinear terms = 8
# of potential variables for partitioning = 8
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Using convhull_ebd formulation
Encoding method = default
Independent branching scheme = false
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.02s (0 iterations).
====================================================================================================
LOWER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
| 1 | 7049.2479 | 7049.2479 | 3004.247 | 57.38202 | 0.07s
| 2 | - | 7049.2479 | 4896.6075 | 30.53716 | 0.55s
| 3 | 7049.2479 | 7049.2479 | 5871.5307 | 16.70699 | 2.94s
| 4 | - | 7049.2479 | 6717.2923 | 4.70909 | 9.28s
| 5 | 7049.267 | 7049.2479 | 6901.8131 | 2.0915 | 21.39s
| 6 | 7049.9758 | 7049.2479 | 7020.723 | 0.40465 | 37.49s
| 7 | 7050.6454 | 7049.2479 | 7042.4094 | 0.09701 | 90.49s
| 8 | 7050.7924 | 7049.2479 | 7047.8904 | 0.01926 | 138.28s
| finish | 7049.2479 | 7049.2479 | 7048.8389 | 0.0058 | 269.89s
====================================================================================================
*** Alpine ended with status Optimal ***
Test Summary: | Pass Total
Embedding LINK Test || AMP-CONV || basic solve || examples/nlp3.jl | 3 3
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 1
# of non-linear constraints = 1
# of linear constraints = 0
# of continuous variables = 2
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 2
# of variables involved in nonlinear terms = 2
# of potential variables for partitioning = 2
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Pavito
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 6
Relative optimality gap criteria = 0.0100%
Discretization ratio = 8
Using convhull_ebd formulation
Encoding method = default
Independent branching scheme = false
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.02s (0 iterations).
====================================================================================================
LOWER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
| 1 | 2.0 | 2.0 | 1.3846 | 30.76922 | 0.08s
| 2 | 1.4142 | 1.4142 | 1.3846 | 2.09291 | 0.29s
| 3 | 1.4142 | 1.4142 | 1.3846 | 2.09291 | 0.54s
| 4 | 1.4142 | 1.4142 | 1.4114 | 0.20205 | 0.79s
| 5 | 1.4142 | 1.4142 | 1.414 | 0.01356 | 1.09s
| finish | 1.4142 | 1.4142 | 1.4141 | 0.00968 | 1.51s
====================================================================================================
*** Alpine ended with status Optimal ***
Test Summary: | Pass Total
Embedding LINK Test || AMP || special problem || ... | 1 1
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
Warning: -/+Inf bounds detected on at least 20 variables. Initializing with values -/+10000.0. This may affect global optimality and run times.
PROBLEM STATISTICS
# of constraints = 66
# of non-linear constraints = 18
# of linear constraints = 48
# of continuous variables = 76
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 18
# of variables involved in nonlinear terms = 12
# of potential variables for partitioning = 12
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 1
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
┌ Warning: Ipopt finished with status Restoration_Failed
└ @ Ipopt ~/.julia/packages/Ipopt/ruIXY/src/MPB_wrapper.jl:178
┌ Warning: Warning: NLP solve failure Error.
└ @ Alpine ~/.julia/packages/Alpine/yThaY/src/algorithm.jl:256
┌ Warning: Warning: Presolve ends with local solver yielding Error.
└ @ Alpine ~/.julia/packages/Alpine/yThaY/src/algorithm.jl:98
Completed presolve in 0.03s (0 iterations).
====================================================================================================
LOWER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
169 | - | Inf | 69.2386 | LARGE | 0.05s | | finish
====================================================================================================
*** Alpine ended with status UserLimits ***
┌ Warning: Not solved to optimality, status: UserLimits
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/nlp.jl:1283
Test Summary: | Pass Total
Algorithm Logic Test || castro4m2 || 1 iteration || Error case | 1 1
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 225
# of non-linear constraints = 24
# of linear constraints = 201
# of continuous variables = 66
# of binary variables = 36
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 28
# of variables involved in nonlinear terms = 26
# of potential variables for partitioning = 10
=======================================================================
SUB-SOLVERS USED BY ALPINE
MINLP local solver = Pavito
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 3
Relative optimality gap criteria = 0.0100%
Potential variables chosen for partitioning = Min. vertex cover
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 1.41s (0 iterations).
====================================================================================================
UPPER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Upper Bound | Gap (%) | Time
| 1 | 13.3594 | 13.3594 | 14.0064 | 4.84303 | 4.49s
| 2 | - | 13.3594 | 14.0064 | 4.84303 | 9.18s
| finish | - | 13.3594 | 13.9934 | 4.74572 | 17.44s
====================================================================================================
*** Alpine ended with status UserLimits ***
┌ Warning: Not solved to optimality, status: UserLimits
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/nlp.jl:1283
Test Summary: | Pass Total
Algorithm Logic Test || blend029_gl || 3 iterations || Infeasible Case | 3 3
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
CONVEX Objective: 1.0 * x[1] * x[1] + 1.0 * x[3] * x[3]
CONVEX Constraint 1: (3.0 * x[1] * x[1] + 4.0 * x[2] * x[2]) - 25.0 <= 0.0
CONVEX Constraint 2: (3.0 * (1.0 * x[1] * x[1]) + 4.0 * x[2] ^ 2.0) - 10.0 <= 0.0
CONVEX Constraint 3: (3.0 * x[1] ^ 2.0 + 4.0 * x[2] ^ 2.0 + 6.0 * x[3] ^ 2.0) - 10.0 <= 0.0
CONVEX Constraint 5: (-3.0 * x[1] * x[1] - 4.0 * x[2] * x[2]) - -25.0 >= 0.0
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 5
# of non-linear constraints = 5
# of linear constraints = 0
# of continuous variables = 5
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 4
# of detected nonlinear terms = 1
# of variables involved in nonlinear terms = 1
# of potential variables for partitioning = 1
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Pavito
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 1
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.24s (0 iterations).
====================================================================================================
UPPER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Upper Bound | Gap (%) | Time
22 | 3.3333 | 3.3333 | 3.3333 | 0.0 | 0.57s | | finish
====================================================================================================
*** Alpine ended with status Optimal ***
Test Summary: | Pass Total
Convex Model Solve | 1 1
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 6
# of non-linear constraints = 3
# of linear constraints = 3
# of continuous variables = 8
# of binary variables = 0
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 5
# of variables involved in nonlinear terms = 8
# of potential variables for partitioning = 8
=======================================================================
SUB-SOLVERS USED BY ALPINE
NLP local solver = Ipopt
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = 100000.0
Maximum iterations = 1
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.09s (0 iterations).
====================================================================================================
LOWER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time | TIME LEFT
|blue 7049.2479 |light_green 6561.7156 |red 7049.2479 |green 6561.7156 |green 6.91609 |blue 2.95s |green 99997.05s |light_green finish
====================================================================================================
*** Alpine ended with status UserLimits ***
┌ Warning: Not solved to optimality, status: UserLimits
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/nlp.jl:1283
Test Summary: | Pass Total
Uniform partitioning | 1 1
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 9
# of non-linear constraints = 9
# of linear constraints = 0
# of continuous variables = 8
# of binary variables = 5
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 20
# of variables involved in nonlinear terms = 8
# of potential variables for partitioning = 8
=======================================================================
SUB-SOLVERS USED BY ALPINE
MINLP local solver = Pavito
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 99
Relative optimality gap criteria = 0.0100%
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 0.25s (0 iterations).
====================================================================================================
LOWER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
| 1 | 3651.0204 | 3651.0204 | 3130.5085 | 14.25661 | 0.53s
| 2 | 3651.0204 | 3651.0204 | 3505.8171 | 3.97706 | 0.74s
| 3 | 3651.0204 | 3651.0204 | 3603.3379 | 1.306 | 1.0s
| 4 | 3651.0204 | 3651.0204 | 3638.2238 | 0.35049 | 1.47s
| 5 | 3651.0204 | 3651.0204 | 3647.5655 | 0.09463 | 1.98s
| 6 | 3651.0204 | 3651.0204 | 3650.1233 | 0.02457 | 2.72s
| finish | 3651.0204 | 3651.0204 | 3650.7913 | 0.00627 | 4.34s
====================================================================================================
*** Alpine ended with status Optimal ***
Test Summary: | Pass Total
Algorithm Test with binprod terms | 127 127
***********************************************************************
This package contains Alpine.jl, a global solver for nonconvex MINLPs
If you find it useful, please cite the following paper:
Journal of Global Optimization, 2019, https://goo.gl/89zrDf
***********************************************************************
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
# of constraints = 225
# of non-linear constraints = 24
# of linear constraints = 201
# of continuous variables = 66
# of binary variables = 36
# of integer variables = 0
# of detected convex constraints = 0
# of detected nonlinear terms = 28
# of variables involved in nonlinear terms = 26
# of potential variables for partitioning = 10
=======================================================================
SUB-SOLVERS USED BY ALPINE
MINLP local solver = Pavito
MIP solver = Cbc
=======================================================================
ALPINE CONFIGURATION
Maximum solution time = Inf
Maximum iterations = 2
Relative optimality gap criteria = 0.0100%
Potential variables chosen for partitioning = Min. vertex cover
Discretization ratio = 4
Bound-tightening presolve = false
=======================================================================
PRESOLVE
Doing local search
Local solver returns a feasible point
Completed presolve in 1.2s (0 iterations).
====================================================================================================
UPPER-BOUNDING ITERATIONS
| Iter | Incumbent | Best Incumbent | Upper Bound | Gap (%) | Time
| 1 | 13.3594 | 13.3594 | 14.0064 | 4.84303 | 4.58s
| finish | - | 13.3594 | 14.0064 | 4.84303 | 9.01s
====================================================================================================
*** Alpine ended with status UserLimits ***
┌ Warning: Not solved to optimality, status: UserLimits
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/nlp.jl:1283
Test Summary: | Pass Total
Utility Function Tests: Solver identifier fetch | 16 16
Test Summary: | Pass Total
Solver Funtion Tests :: Embedding | 26 26
Testing Alpine tests passed