MINLPLib
A Library of Mixed-Integer and Continuous Nonlinear Programming Instances
Home // Instances // Documentation // Download // Statistics
Instance prolog
Formatsⓘ | ams gms lp mod nl osil pip py |
Primal Bounds (infeas ≤ 1e-08)ⓘ | |
Other points (infeas > 1e-08)ⓘ | |
Dual Boundsⓘ | -0.00000000 (ANTIGONE) 0.00000000 (BARON) -0.00000000 (COUENNE) 0.00000000 (GUROBI) 0.00000000 (LINDO) 0.00000000 (SCIP) |
Referencesⓘ | Norton, R D and Scandizzo, P L, Market Equilibrium Computations in Activity Analysis Models, Operations Research, 29:2, 1981, 243-262. |
Sourceⓘ | GAMS Model Library model prolog |
Applicationⓘ | Market Equilibrium |
Added to libraryⓘ | 31 Jul 2001 |
Problem typeⓘ | QCQP |
#Variablesⓘ | 20 |
#Binary Variablesⓘ | 0 |
#Integer Variablesⓘ | 0 |
#Nonlinear Variablesⓘ | 6 |
#Nonlinear Binary Variablesⓘ | 0 |
#Nonlinear Integer Variablesⓘ | 0 |
Objective Senseⓘ | min |
Objective typeⓘ | quadratic |
Objective curvatureⓘ | indefinite |
#Nonzeros in Objectiveⓘ | 8 |
#Nonlinear Nonzeros in Objectiveⓘ | 6 |
#Constraintsⓘ | 22 |
#Linear Constraintsⓘ | 20 |
#Quadratic Constraintsⓘ | 2 |
#Polynomial Constraintsⓘ | 0 |
#Signomial Constraintsⓘ | 0 |
#General Nonlinear Constraintsⓘ | 0 |
Operands in Gen. Nonlin. Functionsⓘ | |
Constraints curvatureⓘ | indefinite |
#Nonzeros in Jacobianⓘ | 120 |
#Nonlinear Nonzeros in Jacobianⓘ | 8 |
#Nonzeros in (Upper-Left) Hessian of Lagrangianⓘ | 8 |
#Nonzeros in Diagonal of Hessian of Lagrangianⓘ | 0 |
#Blocks in Hessian of Lagrangianⓘ | 2 |
Minimal blocksize in Hessian of Lagrangianⓘ | 3 |
Maximal blocksize in Hessian of Lagrangianⓘ | 3 |
Average blocksize in Hessian of Lagrangianⓘ | 3.0 |
#Semicontinuitiesⓘ | 0 |
#Nonlinear Semicontinuitiesⓘ | 0 |
#SOS type 1ⓘ | 0 |
#SOS type 2ⓘ | 0 |
Minimal coefficientⓘ | 4.8000e-02 |
Maximal coefficientⓘ | 5.0000e+03 |
Infeasibility of initial pointⓘ | 782 |
Sparsity Jacobianⓘ | |
Sparsity Hessian of Lagrangianⓘ |
$offlisting * * Equation counts * Total E G L N X C B * 23 1 0 22 0 0 0 0 * * Variable counts * x b i s1s s2s sc si * Total cont binary integer sos1 sos2 scont sint * 21 21 0 0 0 0 0 0 * FX 0 * * Nonzero counts * Total const NL DLL * 129 115 14 0 * * Solve m using NLP minimizing objvar; Variables objvar,x2,x3,x4,x5,x6,x7,x8,x9,x10,x11,x12,x13,x14,x15,x16,x17,x18 ,x19,x20,x21; Positive Variables x5,x6,x7,x8,x9,x10,x11,x12,x13,x14,x15,x16,x17,x18,x19,x20 ,x21; Equations e1,e2,e3,e4,e5,e6,e7,e8,e9,e10,e11,e12,e13,e14,e15,e16,e17,e18,e19 ,e20,e21,e22,e23; e1.. x5 + x6 - 0.94*x11 - 0.94*x12 - 0.94*x13 + 0.244*x17 + 0.244*x18 + 0.244*x19 =L= 0; e2.. 0.064*x11 + 0.064*x12 + 0.064*x13 - 0.58*x14 - 0.58*x15 - 0.58*x16 + 0.172*x17 + 0.172*x18 + 0.172*x19 =L= 0; e3.. x7 + x8 + 0.048*x11 + 0.048*x12 + 0.048*x13 + 0.247*x14 + 0.247*x15 + 0.247*x16 - 0.916*x17 - 0.916*x18 - 0.916*x19 =L= 0; e4.. x11 + 1.2*x12 + 0.8*x13 + 2*x14 + 1.8*x15 + 2.4*x16 + 3*x17 + 2.7*x18 + 3.2*x19 =L= 3712; e5.. 2*x11 + 1.8*x12 + 2.2*x13 + 3*x14 + 3.5*x15 + 2.3*x16 + 3*x17 + 3.2*x18 + 2.7*x19 =L= 5000; e6.. 356.474947137148*x2 + 53.7083537310174*x4 + x5 - 0.564264890180399*x20 =L= 352; e7.. 339.983422262764*x2 + 43.5418249774113*x4 + x6 - 0.405939876920766*x21 =L= 430; e8.. 106.946746119538*x2 + 145.018955433089*x4 + x7 - 0.507117039797071*x20 =L= 222; e9.. 173.929713444361*x2 + 203.031384299627*x4 + x8 - 0.578889145413521*x21 =L= 292; e10.. x5*x2 + x7*x4 - x20 =L= 0; e11.. x6*x2 + x8*x4 - x21 =L= 0; e12.. - 3340.8*x9 - 500*x10 + x20 =L= 0; e13.. - 371.2*x9 - 4500*x10 + x21 =L= 0; e14.. 0.94*x2 - 0.064*x3 - 0.048*x4 - x9 - 2*x10 =L= 0; e15.. 0.94*x2 - 0.064*x3 - 0.048*x4 - 1.2*x9 - 1.8*x10 =L= 0; e16.. 0.94*x2 - 0.064*x3 - 0.048*x4 - 0.8*x9 - 2.2*x10 =L= 0; e17.. 0.58*x3 - 0.247*x4 - 2*x9 - 3*x10 =L= 0; e18.. 0.58*x3 - 0.247*x4 - 1.8*x9 - 3.5*x10 =L= 0; e19.. 0.58*x3 - 0.247*x4 - 2.4*x9 - 2.3*x10 =L= 0; e20.. - 0.244*x2 - 0.172*x3 + 0.916*x4 - 3*x9 - 3*x10 =L= 0; e21.. - 0.244*x2 - 0.172*x3 + 0.916*x4 - 2.7*x9 - 3.2*x10 =L= 0; e22.. - 0.244*x2 - 0.172*x3 + 0.916*x4 - 3.2*x9 - 2.7*x10 =L= 0; e23.. -(x5*x2 + x6*x2 + x7*x4 + x8*x4) - objvar + 3712*x9 + 5000*x10 =E= 0; * set non-default bounds x2.lo = 0.2; x3.lo = 0.2; x4.lo = 0.2; * set non-default levels x2.l = 0.5942; x3.l = 1.6167; x4.l = 1.31077; x5.l = 352; x6.l = 430; x7.l = 222; x8.l = 292; x9.l = 0.130670360422406; x10.l = 0.130670360422406; x20.l = 500.14934; x21.l = 638.25084; 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% minimizing objvar;
Last updated: 2024-08-26 Git hash: 6cc1607f