MINLPLib
A Library of Mixed-Integer and Continuous Nonlinear Programming Instances
Home // Instances // Documentation // Download // Statistics
Instance eq6_1
Formatsⓘ | ams gms mod nl osil py |
Primal Bounds (infeas ≤ 1e-08)ⓘ | |
Other points (infeas > 1e-08)ⓘ | |
Dual Boundsⓘ | 117.71712150 (ANTIGONE) 138.18320100 (BARON) 194.58290990 (COUENNE) -134.11040970 (LINDO) 257.69423790 (SCIP) |
Sourceⓘ | AIMMS clients |
Added to libraryⓘ | 06 Feb 2017 |
Problem typeⓘ | NLP |
#Variablesⓘ | 16 |
#Binary Variablesⓘ | 0 |
#Integer Variablesⓘ | 0 |
#Nonlinear Variablesⓘ | 16 |
#Nonlinear Binary Variablesⓘ | 0 |
#Nonlinear Integer Variablesⓘ | 0 |
Objective Senseⓘ | min |
Objective typeⓘ | quadratic |
Objective curvatureⓘ | convex |
#Nonzeros in Objectiveⓘ | 16 |
#Nonlinear Nonzeros in Objectiveⓘ | 16 |
#Constraintsⓘ | 60 |
#Linear Constraintsⓘ | 32 |
#Quadratic Constraintsⓘ | 0 |
#Polynomial Constraintsⓘ | 0 |
#Signomial Constraintsⓘ | 0 |
#General Nonlinear Constraintsⓘ | 28 |
Operands in Gen. Nonlin. Functionsⓘ | sqr sqrt |
Constraints curvatureⓘ | nonconvex |
#Nonzeros in Jacobianⓘ | 144 |
#Nonlinear Nonzeros in Jacobianⓘ | 112 |
#Nonzeros in (Upper-Left) Hessian of Lagrangianⓘ | 256 |
#Nonzeros in Diagonal of Hessian of Lagrangianⓘ | 16 |
#Blocks in Hessian of Lagrangianⓘ | 1 |
Minimal blocksize in Hessian of Lagrangianⓘ | 16 |
Maximal blocksize in Hessian of Lagrangianⓘ | 16 |
Average blocksize in Hessian of Lagrangianⓘ | 16.0 |
#Semicontinuitiesⓘ | 0 |
#Nonlinear Semicontinuitiesⓘ | 0 |
#SOS type 1ⓘ | 0 |
#SOS type 2ⓘ | 0 |
Minimal coefficientⓘ | 1.0000e+00 |
Maximal coefficientⓘ | 3.2000e+01 |
Infeasibility of initial pointⓘ | 2.995 |
Sparsity Jacobianⓘ | |
Sparsity Hessian of Lagrangianⓘ |
$offlisting * * Equation counts * Total E G L N X C B * 61 1 0 60 0 0 0 0 * * Variable counts * x b i s1s s2s sc si * Total cont binary integer sos1 sos2 scont sint * 17 17 0 0 0 0 0 0 * FX 0 * * Nonzero counts * Total const NL DLL * 161 33 128 0 * * Solve m using NLP minimizing objvar; Variables x1,x2,x3,x4,x5,x6,x7,x8,x9,x10,x11,x12,x13,x14,x15,x16,objvar; Equations e1,e2,e3,e4,e5,e6,e7,e8,e9,e10,e11,e12,e13,e14,e15,e16,e17,e18,e19 ,e20,e21,e22,e23,e24,e25,e26,e27,e28,e29,e30,e31,e32,e33,e34,e35,e36 ,e37,e38,e39,e40,e41,e42,e43,e44,e45,e46,e47,e48,e49,e50,e51,e52,e53 ,e54,e55,e56,e57,e58,e59,e60,e61; e1.. -sqrt(sqr(x1 - x2) + sqr(x9 - x10)) =L= -2.995353; e2.. -sqrt(sqr(x1 - x3) + sqr(x9 - x11)) =L= -2.532248; e3.. -sqrt(sqr(x1 - x4) + sqr(x9 - x12)) =L= -2.638959; e4.. -sqrt(sqr(x1 - x5) + sqr(x9 - x13)) =L= -2.638959; e5.. -sqrt(sqr(x1 - x6) + sqr(x9 - x14)) =L= -2.121321; e6.. -sqrt(sqr(x1 - x7) + sqr(x9 - x15)) =L= -1.914214; e7.. -sqrt(sqr(x1 - x8) + sqr(x9 - x16)) =L= -2.828428; e8.. -sqrt(sqr(x2 - x3) + sqr(x10 - x11)) =L= -2.699173; e9.. -sqrt(sqr(x2 - x4) + sqr(x10 - x12)) =L= -2.805884; e10.. -sqrt(sqr(x2 - x5) + sqr(x10 - x13)) =L= -2.805884; e11.. -sqrt(sqr(x2 - x6) + sqr(x10 - x14)) =L= -2.288246; e12.. -sqrt(sqr(x2 - x7) + sqr(x10 - x15)) =L= -2.081139; e13.. -sqrt(sqr(x2 - x8) + sqr(x10 - x16)) =L= -2.995353; e14.. -sqrt(sqr(x3 - x4) + sqr(x11 - x12)) =L= -2.342779; e15.. -sqrt(sqr(x3 - x5) + sqr(x11 - x13)) =L= -2.342779; e16.. -sqrt(sqr(x3 - x6) + sqr(x11 - x14)) =L= -1.825141; e17.. -sqrt(sqr(x3 - x7) + sqr(x11 - x15)) =L= -1.618034; e18.. -sqrt(sqr(x3 - x8) + sqr(x11 - x16)) =L= -2.532248; e19.. -sqrt(sqr(x4 - x5) + sqr(x12 - x13)) =L= -2.44949; e20.. -sqrt(sqr(x4 - x6) + sqr(x12 - x14)) =L= -1.931852; e21.. -sqrt(sqr(x4 - x7) + sqr(x12 - x15)) =L= -1.724745; e22.. -sqrt(sqr(x4 - x8) + sqr(x12 - x16)) =L= -2.638959; e23.. -sqrt(sqr(x5 - x6) + sqr(x13 - x14)) =L= -1.931852; e24.. -sqrt(sqr(x5 - x7) + sqr(x13 - x15)) =L= -1.724745; e25.. -sqrt(sqr(x5 - x8) + sqr(x13 - x16)) =L= -2.638959; e26.. -sqrt(sqr(x6 - x7) + sqr(x14 - x15)) =L= -1.207107; e27.. -sqrt(sqr(x6 - x8) + sqr(x14 - x16)) =L= -2.121321; e28.. -sqrt(sqr(x7 - x8) + sqr(x15 - x16)) =L= -1.914214; e29.. - x1 =L= 1.210786; e30.. - x2 =L= 1.043861; e31.. - x3 =L= 1.506966; e32.. - x4 =L= 1.400255; e33.. - x5 =L= 1.400255; e34.. - x6 =L= 1.917893; e35.. - x7 =L= 2.125; e36.. - x8 =L= 1.210786; e37.. x1 =L= 1.210786; e38.. x2 =L= 1.043861; e39.. x3 =L= 1.506966; e40.. x4 =L= 1.400255; e41.. x5 =L= 1.400255; e42.. x6 =L= 1.917893; e43.. x7 =L= 2.125; e44.. x8 =L= 1.210786; e45.. - x9 =L= 3.710786; e46.. - x10 =L= 3.543861; e47.. - x11 =L= 4.006966; e48.. - x12 =L= 3.900255; e49.. - x13 =L= 3.900255; e50.. - x14 =L= 4.417893; e51.. - x15 =L= 4.625; e52.. - x16 =L= 3.710786; e53.. x9 =L= 3.710786; e54.. x10 =L= 3.543861; e55.. x11 =L= 4.006966; e56.. x12 =L= 3.900255; e57.. x13 =L= 3.900255; e58.. x14 =L= 4.417893; e59.. x15 =L= 4.625; e60.. x16 =L= 3.710786; e61.. 10*x2*x1 - 18*sqr(x1) - 14*sqr(x2) - 18*sqr(x9) + 10*x10*x9 - 14*sqr(x10) + 4*x3*x1 + 6*x3*x2 - 10*sqr(x3) + 4*x11*x9 + 6*x11*x10 - 10*sqr(x11) + 8*x4*x1 - 23*sqr(x4) + 8*x12*x9 - 23*sqr(x12) + 2*x5*x1 + 4*x5*x2 + 10*x5 *x4 - 18*sqr(x5) + 2*x13*x9 + 4*x13*x10 + 10*x13*x12 - 18*sqr(x13) + 4*x6 *x2 + 4*x6*x4 + 20*x6*x5 - 20*sqr(x6) + 4*x14*x10 + 4*x14*x12 + 20*x14* x13 - 20*sqr(x14) + 12*x8*x1 + 10*x8*x3 + 20*x8*x4 + 2*x8*x6 - 32*sqr(x8) + 12*x16*x9 + 10*x16*x11 + 20*x16*x12 + 2*x16*x14 - 32*sqr(x16) + 4*x7* x2 + 4*x7*x4 + 10*x7*x6 + 20*x7*x8 - 19*sqr(x7) + 4*x15*x10 + 4*x15*x12 + 10*x15*x14 + 20*x15*x16 - 19*sqr(x15) + objvar =E= 0; * set non-default bounds x1.lo = -18.999999; x1.up = 18.999999; x2.lo = -18.999999; x2.up = 18.999999; x3.lo = -18.999999; x3.up = 18.999999; x4.lo = -18.999999; x4.up = 18.999999; x5.lo = -18.999999; x5.up = 18.999999; x6.lo = -18.999999; x6.up = 18.999999; x7.lo = -18.999999; x7.up = 18.999999; x8.lo = -18.999999; x8.up = 18.999999; x9.lo = -18.999999; x9.up = 18.999999; x10.lo = -18.999999; x10.up = 18.999999; x11.lo = -18.999999; x11.up = 18.999999; x12.lo = -18.999999; x12.up = 18.999999; x13.lo = -18.999999; x13.up = 18.999999; x14.lo = -18.999999; x14.up = 18.999999; x15.lo = -18.999999; x15.up = 18.999999; x16.lo = -18.999999; x16.up = 18.999999; 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