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1.5.0-DEV-7206b56e94.log
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1.5.0-DEV-7206b56e94.log
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Julia Version 1.5.0-DEV.485
Commit 7206b56e94 (2020-03-18 17:25 UTC)
Platform Info:
OS: Linux (x86_64-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-9.0.1 (ORCJIT, skylake)
Environment:
JULIA_DEPOT_PATH = ::/usr/local/share/julia
JULIA_NUM_THREADS = 2
Resolving package versions...
Installed Calculus ───────────────────── v0.5.1
Installed OpenSpecFun_jll ────────────── v0.5.3+3
Installed ReverseDiffSparse ──────────── v0.8.6
Installed DiffResults ────────────────── v1.0.2
Installed CommonSubexpressions ───────── v0.2.0
Installed SpecialFunctions ───────────── v0.10.0
Installed Alpine ─────────────────────── v0.1.15
Installed DataStructures ─────────────── v0.17.10
Installed CompilerSupportLibraries_jll ─ v0.3.0+0
Installed NaNMath ────────────────────── v0.3.3
Installed Compat ─────────────────────── v2.2.0
Installed OrderedCollections ─────────── v1.1.0
Installed DiffRules ──────────────────── v1.0.1
Installed MathProgBase ───────────────── v0.7.8
Installed ForwardDiff ────────────────── v0.10.9
Installed JuMP ───────────────────────── v0.18.6
Installed StaticArrays ───────────────── v0.12.1
#=#=# ######################################################################## 100.0%
#=#=# ##O#- # ## 3.2%##### 7.5%########### 16.1%################### 26.9%########################### 38.8%######################################## 55.7%###################################################### 76.1%######################################################################## 100.0%
Updating `~/.julia/environments/v1.5/Project.toml`
07493b3f + Alpine v0.1.15
Updating `~/.julia/environments/v1.5/Manifest.toml`
07493b3f + Alpine v0.1.15
49dc2e85 + Calculus v0.5.1
bbf7d656 + CommonSubexpressions v0.2.0
34da2185 + Compat v2.2.0
e66e0078 + CompilerSupportLibraries_jll v0.3.0+0
864edb3b + DataStructures v0.17.10
163ba53b + DiffResults v1.0.2
b552c78f + DiffRules v1.0.1
f6369f11 + ForwardDiff v0.10.9
4076af6c + JuMP v0.18.6
fdba3010 + MathProgBase v0.7.8
77ba4419 + NaNMath v0.3.3
efe28fd5 + OpenSpecFun_jll v0.5.3+3
bac558e1 + OrderedCollections v1.1.0
89212889 + ReverseDiffSparse v0.8.6
276daf66 + SpecialFunctions v0.10.0
90137ffa + StaticArrays v0.12.1
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
Testing Alpine
#=#=# ################################# 47.1%######################################################################## 100.0%
Status `/tmp/jl_zQsl34/Project.toml`
07493b3f Alpine v0.1.15
9961bab8 Cbc v0.6.7
3c7084bd GLPKMathProgInterface v0.4.4
b6b21f68 Ipopt v0.6.1
4076af6c JuMP v0.18.6
fdba3010 MathProgBase v0.7.8
cd433a01 Pavito v0.1.2
8ba89e20 Distributed
37e2e46d LinearAlgebra
44cfe95a Pkg
de0858da Printf
9a3f8284 Random
2f01184e SparseArrays
10745b16 Statistics
8dfed614 Test
Status `/tmp/jl_zQsl34/Manifest.toml`
07493b3f Alpine v0.1.15
6e4b80f9 BenchmarkTools v0.5.0
b99e7846 BinaryProvider v0.5.8
49dc2e85 Calculus v0.5.1
9961bab8 Cbc v0.6.7
523fee87 CodecBzip2 v0.6.0
944b1d66 CodecZlib v0.6.0
bbf7d656 CommonSubexpressions v0.2.0
34da2185 Compat v2.2.0
e66e0078 CompilerSupportLibraries_jll v0.3.0+0
e95a7839 ConicBenchmarkUtilities v0.4.0
952205b0 ConicNonlinearBridge v0.2.1
864edb3b DataStructures v0.17.10
163ba53b DiffResults v1.0.2
b552c78f DiffRules v1.0.1
f6369f11 ForwardDiff v0.10.9
60bf3e95 GLPK v0.12.1
3c7084bd GLPKMathProgInterface v0.4.4
92fee26a GZip v0.5.1
cd3eb016 HTTP v0.8.12
83e8ac13 IniFile v0.5.0
b6b21f68 Ipopt v0.6.1
682c06a0 JSON v0.21.0
7d188eb4 JSONSchema v0.2.0
4076af6c JuMP v0.18.6
b8f27783 MathOptInterface v0.9.12
fdba3010 MathProgBase v0.7.8
739be429 MbedTLS v1.0.1
c8ffd9c3 MbedTLS_jll v2.16.0+1
d8a4904e MutableArithmetics v0.2.7
77ba4419 NaNMath v0.3.3
efe28fd5 OpenSpecFun_jll v0.5.3+3
bac558e1 OrderedCollections v1.1.0
69de0a69 Parsers v0.3.12
cd433a01 Pavito v0.1.2
89212889 ReverseDiffSparse v0.8.6
276daf66 SpecialFunctions v0.10.0
90137ffa StaticArrays v0.12.1
3bb67fe8 TranscodingStreams v0.9.5
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
***********************************************************************
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
#Variables = 8
#Bin-Int Variables = 0
#Constraints = 6
#NL Constraints = 3
#Linear Constraints = 3
#Detected convex constraints = 0
#Detected nonlinear terms = 5
#Variables involved in nonlinear terms = 8
#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 1.26s (0 iterations).
LOWER-BOUNDING ITERATIONS
====================================================================================================
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
| finish | 7049.2479 | 7049.2479 | 3004.247 | 57.38202 | 4.15s
====================================================================================================
*** 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
***********************************************************************
PROBLEM STATISTICS
#Variables = 8
#Bin-Int Variables = 0
#Constraints = 6
#NL Constraints = 3
#Linear Constraints = 3
#Detected convex constraints = 0
#Detected nonlinear terms = 5
#Variables involved in nonlinear terms = 8
#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
#Variables = 8
#Bin-Int Variables = 0
#Constraints = 6
#NL Constraints = 3
#Linear Constraints = 3
#Detected convex constraints = 0
#Detected nonlinear terms = 5
#Variables involved in nonlinear terms = 8
#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 | 0.04s
====================================================================================================
*** 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
#Variables = 8
#Bin-Int Variables = 0
#Constraints = 6
#NL Constraints = 3
#Linear Constraints = 3
#Detected convex constraints = 0
#Detected nonlinear terms = 5
#Variables involved in nonlinear terms = 8
#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 | 0.1s
| finish | - | 7049.2479 | 4896.6075 | 30.53716 | 0.51s
====================================================================================================
*** 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
#Variables = 42
#Bin-Int Variables = 0
#Constraints = 45
#NL Constraints = 12
#Linear Constraints = 33
#Detected convex constraints = 0
#Detected nonlinear terms = 12
#Variables involved in nonlinear terms = 10
#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 0.08s (0 iterations).
LOWER-BOUNDING ITERATIONS
====================================================================================================
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
| finish | 470.3175 | 470.3175 | 78.0 | 83.41546 | 0.18s
====================================================================================================
*** 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
#Variables = 42
#Bin-Int Variables = 0
#Constraints = 45
#NL Constraints = 12
#Linear Constraints = 33
#Detected convex constraints = 0
#Detected nonlinear terms = 12
#Variables involved in nonlinear terms = 10
#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 0.07s (0 iterations).
LOWER-BOUNDING ITERATIONS
====================================================================================================
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
| finish | 470.3175 | 470.3175 | 78.0 | 83.41546 | 0.09s
====================================================================================================
*** 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
#Variables = 42
#Bin-Int Variables = 0
#Constraints = 45
#NL Constraints = 12
#Linear Constraints = 33
#Detected convex constraints = 0
#Detected nonlinear terms = 12
#Variables involved in nonlinear terms = 10
#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
#Variables = 102
#Bin-Int Variables = 36
#Constraints = 225
#NL Constraints = 24
#Linear Constraints = 201
#Detected convex constraints = 0
#Detected nonlinear terms = 28
#Variables involved in nonlinear terms = 26
#Potential variables for partitioning = 26
SUB-SOLVERS USED BY ALPINE
MINLP local solver = PavitoSolver(0, Inf, 1
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
#Variables = 102
#Bin-Int Variables = 36
#Constraints = 225
#NL Constraints = 24
#Linear Constraints = 201
#Detected convex constraints = 0
#Detected nonlinear terms = 28
#Variables involved in nonlinear terms = 26
#Potential variables for partitioning = 10
SUB-SOLVERS USED BY ALPINE
MINLP local solver = PavitoSolver(0, Inf, 1
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
#Variables = 102
#Bin-Int Variables = 36
#Constraints = 225
#NL Constraints = 24
#Linear Constraints = 201
#Detected convex constraints = 0
#Detected nonlinear terms = 28
#Variables involved in nonlinear terms = 26
#Potential variables for partitioning = 10
SUB-SOLVERS USED BY ALPINE
MINLP local solver = PavitoSolver(0, Inf, 1
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
#Variables = 174
#Bin-Int Variables = 0
#Constraints = 152
#NL Constraints = 48
#Linear Constraints = 104
#Detected convex constraints = 0
#Detected nonlinear terms = 48
#Variables involved in nonlinear terms = 24
#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 0.78s (0 iterations).
LOWER-BOUNDING ITERATIONS
====================================================================================================
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
| finish | 227.981 | 227.981 | 95.2208 | 58.23302 | 0.87s
====================================================================================================
*** 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
#Variables = 174
#Bin-Int Variables = 0
#Constraints = 152
#NL Constraints = 48
#Linear Constraints = 104
#Detected convex constraints = 0
#Detected nonlinear terms = 48
#Variables involved in nonlinear terms = 24
#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 0.76s (0 iterations).
LOWER-BOUNDING ITERATIONS
====================================================================================================
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
| 1 | 227.981 | 227.981 | 95.2208 | 58.23302 | 0.86s
UPDATED DISC-VAR COUNT = 12 : [110, 111, 112, 113, 114, 115, 116, 117, 118, 122, 123, 124]
| finish | 262.7784 | 227.981 | 116.1566 | 49.04985 | 2.57s
====================================================================================================
*** 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
#Variables = 42
#Bin-Int Variables = 0
#Constraints = 45
#NL Constraints = 12
#Linear Constraints = 33
#Detected convex constraints = 0
#Detected nonlinear terms = 12
#Variables involved in nonlinear terms = 10
#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 0.06s (0 iterations).
LOWER-BOUNDING ITERATIONS
====================================================================================================
| Iter | Incumbent | Best Incumbent | Lower Bound | Gap (%) | Time
| finish | 470.3175 | 470.3175 | 78.0 | 83.41546 | 0.07s
====================================================================================================
*** 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
#Variables = 6
#Bin-Int Variables = 0
#Constraints = 9
#NL Constraints = 8
#Linear Constraints = 1
#Detected convex constraints = 0
#Detected nonlinear terms = 8
#Variables involved in nonlinear terms = 4
#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
***********************************************************************
┌ Warning: Special expression structure detected [*, coef, :call, ...]. Currently using a beta fix...
└ @ Alpine ~/.julia/packages/Alpine/q7ook/src/nlexpr.jl:330
Initial constraint-based bound evaluation exhausted...
PROBLEM STATISTICS
#Variables = 2
#Bin-Int Variables = 0
#Constraints = 1
#NL Constraints = 1
#Linear Constraints = 0
#Detected convex constraints = 0
#Detected nonlinear terms = 3
#Variables involved in nonlinear terms = 2
#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
#Variables = 8
#Bin-Int Variables = 0
#Constraints = 6
#NL Constraints = 3
#Linear Constraints = 3
#Detected convex constraints = 0
#Detected nonlinear terms = 5
#Variables involved in nonlinear terms = 8
#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
#Variables = 6
#Bin-Int Variables = 0
#Constraints = 5
#NL Constraints = 5
#Linear Constraints = 0
#Detected convex constraints = 0
#Detected nonlinear terms = 8
#Variables involved in nonlinear terms = 4
#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
#Variables = 102
#Bin-Int Variables = 36
#Constraints = 213
#NL Constraints = 12
#Linear Constraints = 201
#Detected convex constraints = 0
#Detected nonlinear terms = 28
#Variables involved in nonlinear terms = 26
#Potential variables for partitioning = 10
SUB-SOLVERS USED BY ALPINE
MINLP local solver = PavitoSolver(0, Inf, 1
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
#Variables = 3
#Bin-Int Variables = 0
#Constraints = 1
#NL Constraints = 0
#Linear Constraints = 1
#Detected convex constraints = 0
#Detected nonlinear terms = 1
#Variables involved in nonlinear terms = 3
#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
#Variables = 3
#Bin-Int Variables = 0
#Constraints = 1
#NL Constraints = 0
#Linear Constraints = 1
#Detected convex constraints = 0
#Detected nonlinear terms = 2
#Variables involved in nonlinear terms = 4
#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
#Variables = 3
#Bin-Int Variables = 0
#Constraints = 1
#NL Constraints = 0
#Linear Constraints = 1
#Detected convex constraints = 0
#Detected nonlinear terms = 2
#Variables involved in nonlinear terms = 4
#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
#Variables = 4
#Bin-Int Variables = 0
#Constraints = 1
#NL Constraints = 0
#Linear Constraints = 1
#Detected convex constraints = 0
#Detected nonlinear terms = 1
#Variables involved in nonlinear terms = 4
#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
#Variables = 4
#Bin-Int Variables = 0
#Constraints = 1
#NL Constraints = 0
#Linear Constraints = 1
#Detected convex constraints = 0
#Detected nonlinear terms = 3
#Variables involved in nonlinear terms = 6
#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
#Variables = 4
#Bin-Int Variables = 0
#Constraints = 1
#NL Constraints = 0
#Linear Constraints = 1
#Detected convex constraints = 0
#Detected nonlinear terms = 2
#Variables involved in nonlinear terms = 5
#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
#Variables = 4
#Bin-Int Variables = 0
#Constraints = 1
#NL Constraints = 0
#Linear Constraints = 1
#Detected convex constraints = 0
#Detected nonlinear terms = 2
#Variables involved in nonlinear terms = 5
#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
#Variables = 4
#Bin-Int Variables = 0
#Constraints = 1
#NL Constraints = 0
#Linear Constraints = 1
#Detected convex constraints = 0
#Detected nonlinear terms = 3