MINLPLib

A Library of Mixed-Integer and Continuous Nonlinear Programming Instances

Home // Instances // Documentation // Download // Statistics


Instance maxmin

Formatsⓘ ams gms mod nl osil py
Primal Bounds (infeas ≤ 1e-08)ⓘ
-0.36609601 p3 ( gdx sol )
(infeas: 6e-17)
-0.3503624 p2 ( gdx sol )
(infeas: 0)
-0.34108138 p1 ( gdx sol )
(infeas: 0)
Other points (infeas > 1e-08)ⓘ  
Dual Boundsⓘ
-1.09071382 (ANTIGONE)
-0.71796852 (BARON)
-1.04446594 (COUENNE)
-1.04053536 (GUROBI)
-0.81568286 (LINDO)
-0.48806049 (SCIP)
Referencesⓘ Stinstra, E, den Hertog, D, Stehouwer, P, and Vestjens, A, Constrained Maximin Designs for Computer Experiments, Technometrics, 45:4, 2003, 340-346.
Pinter, J D, LGO - A Model Development System for Continuous Global Optimization, User's Guide, Pinter Consulting Services, Halifax, NS, Canada, Revised edition, 2003.
Sourceⓘ GAMS Model Library model maxmin
Applicationⓘ Geometry
Added to libraryⓘ 31 Jul 2001
Problem typeⓘ NLP
#Variablesⓘ 27
#Binary Variablesⓘ 0
#Integer Variablesⓘ 0
#Nonlinear Variablesⓘ 26
#Nonlinear Binary Variablesⓘ 0
#Nonlinear Integer Variablesⓘ 0
Objective Senseⓘ min
Objective typeⓘ linear
Objective curvatureⓘ linear
#Nonzeros in Objectiveⓘ 1
#Nonlinear Nonzeros in Objectiveⓘ 0
#Constraintsⓘ 78
#Linear Constraintsⓘ 0
#Quadratic Constraintsⓘ 0
#Polynomial Constraintsⓘ 0
#Signomial Constraintsⓘ 0
#General Nonlinear Constraintsⓘ 78
Operands in Gen. Nonlin. Functionsⓘ sqr sqrt
Constraints curvatureⓘ nonconvex
#Nonzeros in Jacobianⓘ 390
#Nonlinear Nonzeros in Jacobianⓘ 312
#Nonzeros in (Upper-Left) Hessian of Lagrangianⓘ 676
#Nonzeros in Diagonal of Hessian of Lagrangianⓘ 26
#Blocks in Hessian of Lagrangianⓘ 1
Minimal blocksize in Hessian of Lagrangianⓘ 26
Maximal blocksize in Hessian of Lagrangianⓘ 26
Average blocksize in Hessian of Lagrangianⓘ 26.0
#Semicontinuitiesⓘ 0
#Nonlinear Semicontinuitiesⓘ 0
#SOS type 1ⓘ 0
#SOS type 2ⓘ 0
Minimal coefficientⓘ 1.0000e+00
Maximal coefficientⓘ 1.0000e+00
Infeasibility of initial pointⓘ 0
Sparsity Jacobianⓘ Sparsity of Objective Gradient and Jacobian
Sparsity Hessian of Lagrangianⓘ Sparsity of Hessian of Lagrangian

$offlisting
*  
*  Equation counts
*      Total        E        G        L        N        X        C        B
*         78        0        0       78        0        0        0        0
*  
*  Variable counts
*                   x        b        i      s1s      s2s       sc       si
*      Total     cont   binary  integer     sos1     sos2    scont     sint
*         27       27        0        0        0        0        0        0
*  FX      2
*  
*  Nonzero counts
*      Total    const       NL      DLL
*        390       78      312        0
*
*  Solve m using NLP minimizing objvar;


Variables  x1,x2,x3,x4,x5,x6,x7,x8,x9,x10,x11,x12,x13,x14,x15,x16,x17,x18,x19
          ,x20,x21,x22,x23,x24,x25,x26,objvar;

Positive Variables  x3,x4,x5,x6,x7,x8,x9,x10,x11,x12,x13,x14,x15,x16,x17,x18
          ,x19,x20,x21,x22,x23,x24,x25,x26;

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,e62,e63,e64,e65,e66,e67,e68,e69,e70
          ,e71,e72,e73,e74,e75,e76,e77,e78;


e1.. -sqrt(sqr(x3 - x1) + sqr(x4 - x2)) - objvar =L= 0;

e2.. -sqrt(sqr(x5 - x1) + sqr(x6 - x2)) - objvar =L= 0;

e3.. -sqrt(sqr(x5 - x3) + sqr(x6 - x4)) - objvar =L= 0;

e4.. -sqrt(sqr(x7 - x1) + sqr(x8 - x2)) - objvar =L= 0;

e5.. -sqrt(sqr(x7 - x3) + sqr(x8 - x4)) - objvar =L= 0;

e6.. -sqrt(sqr(x7 - x5) + sqr(x8 - x6)) - objvar =L= 0;

e7.. -sqrt(sqr(x9 - x1) + sqr(x10 - x2)) - objvar =L= 0;

e8.. -sqrt(sqr(x9 - x3) + sqr(x10 - x4)) - objvar =L= 0;

e9.. -sqrt(sqr(x9 - x5) + sqr(x10 - x6)) - objvar =L= 0;

e10.. -sqrt(sqr(x9 - x7) + sqr(x10 - x8)) - objvar =L= 0;

e11.. -sqrt(sqr(x11 - x1) + sqr(x12 - x2)) - objvar =L= 0;

e12.. -sqrt(sqr(x11 - x3) + sqr(x12 - x4)) - objvar =L= 0;

e13.. -sqrt(sqr(x11 - x5) + sqr(x12 - x6)) - objvar =L= 0;

e14.. -sqrt(sqr(x11 - x7) + sqr(x12 - x8)) - objvar =L= 0;

e15.. -sqrt(sqr(x11 - x9) + sqr(x12 - x10)) - objvar =L= 0;

e16.. -sqrt(sqr(x13 - x1) + sqr(x14 - x2)) - objvar =L= 0;

e17.. -sqrt(sqr(x13 - x3) + sqr(x14 - x4)) - objvar =L= 0;

e18.. -sqrt(sqr(x13 - x5) + sqr(x14 - x6)) - objvar =L= 0;

e19.. -sqrt(sqr(x13 - x7) + sqr(x14 - x8)) - objvar =L= 0;

e20.. -sqrt(sqr(x13 - x9) + sqr(x14 - x10)) - objvar =L= 0;

e21.. -sqrt(sqr(x13 - x11) + sqr(x14 - x12)) - objvar =L= 0;

e22.. -sqrt(sqr(x15 - x1) + sqr(x16 - x2)) - objvar =L= 0;

e23.. -sqrt(sqr(x15 - x3) + sqr(x16 - x4)) - objvar =L= 0;

e24.. -sqrt(sqr(x15 - x5) + sqr(x16 - x6)) - objvar =L= 0;

e25.. -sqrt(sqr(x15 - x7) + sqr(x16 - x8)) - objvar =L= 0;

e26.. -sqrt(sqr(x15 - x9) + sqr(x16 - x10)) - objvar =L= 0;

e27.. -sqrt(sqr(x15 - x11) + sqr(x16 - x12)) - objvar =L= 0;

e28.. -sqrt(sqr(x15 - x13) + sqr(x16 - x14)) - objvar =L= 0;

e29.. -sqrt(sqr(x17 - x1) + sqr(x18 - x2)) - objvar =L= 0;

e30.. -sqrt(sqr(x17 - x3) + sqr(x18 - x4)) - objvar =L= 0;

e31.. -sqrt(sqr(x17 - x5) + sqr(x18 - x6)) - objvar =L= 0;

e32.. -sqrt(sqr(x17 - x7) + sqr(x18 - x8)) - objvar =L= 0;

e33.. -sqrt(sqr(x17 - x9) + sqr(x18 - x10)) - objvar =L= 0;

e34.. -sqrt(sqr(x17 - x11) + sqr(x18 - x12)) - objvar =L= 0;

e35.. -sqrt(sqr(x17 - x13) + sqr(x18 - x14)) - objvar =L= 0;

e36.. -sqrt(sqr(x17 - x15) + sqr(x18 - x16)) - objvar =L= 0;

e37.. -sqrt(sqr(x19 - x1) + sqr(x20 - x2)) - objvar =L= 0;

e38.. -sqrt(sqr(x19 - x3) + sqr(x20 - x4)) - objvar =L= 0;

e39.. -sqrt(sqr(x19 - x5) + sqr(x20 - x6)) - objvar =L= 0;

e40.. -sqrt(sqr(x19 - x7) + sqr(x20 - x8)) - objvar =L= 0;

e41.. -sqrt(sqr(x19 - x9) + sqr(x20 - x10)) - objvar =L= 0;

e42.. -sqrt(sqr(x19 - x11) + sqr(x20 - x12)) - objvar =L= 0;

e43.. -sqrt(sqr(x19 - x13) + sqr(x20 - x14)) - objvar =L= 0;

e44.. -sqrt(sqr(x19 - x15) + sqr(x20 - x16)) - objvar =L= 0;

e45.. -sqrt(sqr(x19 - x17) + sqr(x20 - x18)) - objvar =L= 0;

e46.. -sqrt(sqr(x21 - x1) + sqr(x22 - x2)) - objvar =L= 0;

e47.. -sqrt(sqr(x21 - x3) + sqr(x22 - x4)) - objvar =L= 0;

e48.. -sqrt(sqr(x21 - x5) + sqr(x22 - x6)) - objvar =L= 0;

e49.. -sqrt(sqr(x21 - x7) + sqr(x22 - x8)) - objvar =L= 0;

e50.. -sqrt(sqr(x21 - x9) + sqr(x22 - x10)) - objvar =L= 0;

e51.. -sqrt(sqr(x21 - x11) + sqr(x22 - x12)) - objvar =L= 0;

e52.. -sqrt(sqr(x21 - x13) + sqr(x22 - x14)) - objvar =L= 0;

e53.. -sqrt(sqr(x21 - x15) + sqr(x22 - x16)) - objvar =L= 0;

e54.. -sqrt(sqr(x21 - x17) + sqr(x22 - x18)) - objvar =L= 0;

e55.. -sqrt(sqr(x21 - x19) + sqr(x22 - x20)) - objvar =L= 0;

e56.. -sqrt(sqr(x23 - x1) + sqr(x24 - x2)) - objvar =L= 0;

e57.. -sqrt(sqr(x23 - x3) + sqr(x24 - x4)) - objvar =L= 0;

e58.. -sqrt(sqr(x23 - x5) + sqr(x24 - x6)) - objvar =L= 0;

e59.. -sqrt(sqr(x23 - x7) + sqr(x24 - x8)) - objvar =L= 0;

e60.. -sqrt(sqr(x23 - x9) + sqr(x24 - x10)) - objvar =L= 0;

e61.. -sqrt(sqr(x23 - x11) + sqr(x24 - x12)) - objvar =L= 0;

e62.. -sqrt(sqr(x23 - x13) + sqr(x24 - x14)) - objvar =L= 0;

e63.. -sqrt(sqr(x23 - x15) + sqr(x24 - x16)) - objvar =L= 0;

e64.. -sqrt(sqr(x23 - x17) + sqr(x24 - x18)) - objvar =L= 0;

e65.. -sqrt(sqr(x23 - x19) + sqr(x24 - x20)) - objvar =L= 0;

e66.. -sqrt(sqr(x23 - x21) + sqr(x24 - x22)) - objvar =L= 0;

e67.. -sqrt(sqr(x25 - x1) + sqr(x26 - x2)) - objvar =L= 0;

e68.. -sqrt(sqr(x25 - x3) + sqr(x26 - x4)) - objvar =L= 0;

e69.. -sqrt(sqr(x25 - x5) + sqr(x26 - x6)) - objvar =L= 0;

e70.. -sqrt(sqr(x25 - x7) + sqr(x26 - x8)) - objvar =L= 0;

e71.. -sqrt(sqr(x25 - x9) + sqr(x26 - x10)) - objvar =L= 0;

e72.. -sqrt(sqr(x25 - x11) + sqr(x26 - x12)) - objvar =L= 0;

e73.. -sqrt(sqr(x25 - x13) + sqr(x26 - x14)) - objvar =L= 0;

e74.. -sqrt(sqr(x25 - x15) + sqr(x26 - x16)) - objvar =L= 0;

e75.. -sqrt(sqr(x25 - x17) + sqr(x26 - x18)) - objvar =L= 0;

e76.. -sqrt(sqr(x25 - x19) + sqr(x26 - x20)) - objvar =L= 0;

e77.. -sqrt(sqr(x25 - x21) + sqr(x26 - x22)) - objvar =L= 0;

e78.. -sqrt(sqr(x25 - x23) + sqr(x26 - x24)) - objvar =L= 0;

* set non-default bounds
x1.fx = 0;
x2.fx = 0;
x3.up = 1;
x4.up = 1;
x5.up = 1;
x6.up = 1;
x7.up = 1;
x8.up = 1;
x9.up = 1;
x10.up = 1;
x11.up = 1;
x12.up = 1;
x13.up = 1;
x14.up = 1;
x15.up = 1;
x16.up = 1;
x17.up = 1;
x18.up = 1;
x19.up = 1;
x20.up = 1;
x21.up = 1;
x22.up = 1;
x23.up = 1;
x24.up = 1;
x25.up = 1;
x26.up = 1;

* set non-default levels
x3.l = 0.550375356;
x4.l = 0.301137904;
x5.l = 0.292212117;
x6.l = 0.224052867;
x7.l = 0.349830504;
x8.l = 0.856270347;
x9.l = 0.067113723;
x10.l = 0.500210669;
x11.l = 0.998117627;
x12.l = 0.578733378;
x13.l = 0.991133039;
x14.l = 0.762250467;
x15.l = 0.130692483;
x16.l = 0.639718759;
x17.l = 0.159517864;
x18.l = 0.250080533;
x19.l = 0.668928609;
x20.l = 0.435356381;
x21.l = 0.359700266;
x22.l = 0.351441368;
x23.l = 0.13149159;
x24.l = 0.150101788;
x25.l = 0.58911365;
x26.l = 0.830892812;

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: 2026-09-14 Git hash: 9472b011
Imprint / Privacy Policy / License: CC-BY 4.0