MINLPLib
A Library of Mixed-Integer and Continuous Nonlinear Programming Instances
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Instance alkylation
Formatsⓘ | ams gms mod nl osil py |
Primal Bounds (infeas ≤ 1e-08)ⓘ | |
Other points (infeas > 1e-08)ⓘ | |
Dual Boundsⓘ | 1768.80696500 (ANTIGONE) 1768.80701500 (BARON) 1768.80699200 (COUENNE) 1768.80852000 (LINDO) 1768.85220900 (SCIP) |
Referencesⓘ | Edgar, T F, Himmelblau, D M, and Lasdon, L S, Optimization of Chemical Processes, McGraw Hill, Boston, 2001. |
Sourceⓘ | Housam Binous |
Added to libraryⓘ | 31 Jul 2001 |
Problem typeⓘ | NLP |
#Variablesⓘ | 10 |
#Binary Variablesⓘ | 0 |
#Integer Variablesⓘ | 0 |
#Nonlinear Variablesⓘ | 8 |
#Nonlinear Binary Variablesⓘ | 0 |
#Nonlinear Integer Variablesⓘ | 0 |
Objective Senseⓘ | max |
Objective typeⓘ | quadratic |
Objective curvatureⓘ | indefinite |
#Nonzeros in Objectiveⓘ | 6 |
#Nonlinear Nonzeros in Objectiveⓘ | 2 |
#Constraintsⓘ | 11 |
#Linear Constraintsⓘ | 5 |
#Quadratic Constraintsⓘ | 2 |
#Polynomial Constraintsⓘ | 2 |
#Signomial Constraintsⓘ | 1 |
#General Nonlinear Constraintsⓘ | 1 |
Operands in Gen. Nonlin. Functionsⓘ | div mul |
Constraints curvatureⓘ | indefinite |
#Nonzeros in Jacobianⓘ | 31 |
#Nonlinear Nonzeros in Jacobianⓘ | 12 |
#Nonzeros in (Upper-Left) Hessian of Lagrangianⓘ | 19 |
#Nonzeros in Diagonal of Hessian of Lagrangianⓘ | 5 |
#Blocks in Hessian of Lagrangianⓘ | 2 |
Minimal blocksize in Hessian of Lagrangianⓘ | 4 |
Maximal blocksize in Hessian of Lagrangianⓘ | 4 |
Average blocksize in Hessian of Lagrangianⓘ | 4.0 |
#Semicontinuitiesⓘ | 0 |
#Nonlinear Semicontinuitiesⓘ | 0 |
#SOS type 1ⓘ | 0 |
#SOS type 2ⓘ | 0 |
Minimal coefficientⓘ | 6.6700e-03 |
Maximal coefficientⓘ | 9.8000e+04 |
Infeasibility of initial pointⓘ | 0.44 |
Sparsity Jacobianⓘ | |
Sparsity Hessian of Lagrangianⓘ |
$offlisting * * Equation counts * Total E G L N X C B * 12 4 8 0 0 0 0 0 * * Variable counts * x b i s1s s2s sc si * Total cont binary integer sos1 sos2 scont sint * 11 11 0 0 0 0 0 0 * FX 0 * * Nonzero counts * Total const NL DLL * 38 24 14 0 * * Solve m using NLP maximizing objvar; Variables x1,x2,x3,x4,x5,x6,x7,x8,x9,x10,objvar; Equations e1,e2,e3,e4,e5,e6,e7,e8,e9,e10,e11,e12; e1.. -0.063*x4*x7 + 5.04*x1 + 0.035*x2 + 10*x3 + 3.36*x5 + objvar =E= 0; e2.. x1 - 1.22*x4 + x5 =E= 0; e3.. -98000*x3/(x4*x9 + 1000*x3) + x6 =E= 0; e4.. -(x2 + x5)/x1 + x8 =E= 0; e5.. (1.12 + 0.13167*x8 - 0.00667*x8**2)*x1 - 0.99*x4 =G= 0; e6.. -(1.12 + 0.13167*x8 - 0.00667*x8**2)*x1 + 1.01010101010101*x4 =G= 0; e7.. 1.098*x8 - 0.038*x8**2 + 0.325*x6 - 0.99*x7 =G= -57.425; e8.. -(1.098*x8 - 0.038*x8**2) - 0.325*x6 + 1.01010101010101*x7 =G= 57.425; e9.. - 0.9*x9 - 0.222*x10 =G= -35.82; e10.. 1.11111111111111*x9 + 0.222*x10 =G= 35.82; e11.. 3*x7 - 0.99*x10 =G= 133; e12.. - 3*x7 + 1.01010101010101*x10 =G= -133; * set non-default bounds x1.lo = 1E-6; x1.up = 2000; x2.lo = 1E-6; x2.up = 16000; x3.lo = 1E-6; x3.up = 120; x4.lo = 1E-6; x4.up = 5000; x5.lo = 1E-6; x5.up = 2000; x6.lo = 85; x6.up = 93; x7.lo = 90; x7.up = 95; x8.lo = 3; x8.up = 12; x9.lo = 0.01; x9.up = 4; x10.lo = 145; x10.up = 162; * set non-default levels x1.l = 1745; x2.l = 12000; x3.l = 110; x4.l = 3048; x5.l = 1974; x6.l = 89.2; x7.l = 92.8; x8.l = 8; x9.l = 3.6; Model m / all /; m.limrow=0; m.limcol=0; m.tolproj=0.0; $if NOT '%gams.u1%' == '' $include '%gams.u1%' $if not set NLP $set NLP NLP Solve m using %NLP% maximizing objvar;
Last updated: 2024-08-26 Git hash: 6cc1607f