MINLPLib

A Library of Mixed-Integer and Continuous Nonlinear Programming Instances

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Instance st_robot

Formats ams gms lp mod nl osil pip py
Primal Bounds (infeas ≤ 1e-08)
0.00000000 p1 ( gdx sol )
(infeas: 5e-11)
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 Tawarmalani, M and Sahinidis, N V, Convexification and Global Optimization in Continuous and Mixed-Integer Nonlinear Programming: Theory, Algorithms, Software, and Applications, Kluwer, 2002.
Tsai, L-W and Morgan, A P, Solving the kinematics of the most general six- and five-degree-of-freedom manipulators by continuation methods, Journal of Mechanics, Transmissions, and Automation in Design}", year = "1985, 107:2, 189-200.
Source BARON book instance input/robot
Added to library 03 Sep 2002
Problem type QCP
#Variables 8
#Binary Variables 0
#Integer Variables 0
#Nonlinear Variables 8
#Nonlinear Binary Variables 0
#Nonlinear Integer Variables 0
Objective Sense min
Objective type constant
Objective curvature linear
#Nonzeros in Objective 0
#Nonlinear Nonzeros in Objective 0
#Constraints 8
#Linear Constraints 1
#Quadratic Constraints 7
#Polynomial Constraints 0
#Signomial Constraints 0
#General Nonlinear Constraints 0
Operands in Gen. Nonlin. Functions  
Constraints curvature indefinite
#Nonzeros in Jacobian 24
#Nonlinear Nonzeros in Jacobian 16
#Nonzeros in (Upper-Left) Hessian of Lagrangian 14
#Nonzeros in Diagonal of Hessian of Lagrangian 8
#Blocks in Hessian of Lagrangian 5
Minimal blocksize in Hessian of Lagrangian 1
Maximal blocksize in Hessian of Lagrangian 3
Average blocksize in Hessian of Lagrangian 1.6
#Semicontinuities 0
#Nonlinear Semicontinuities 0
#SOS type 1 0
#SOS type 2 0
Minimal coefficient 1.6370e-03
Maximal coefficient 1.0000e+00
Infeasibility of initial point 1
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
*          9        9        0        0        0        0        0        0
*  
*  Variable counts
*                   x        b        i      s1s      s2s       sc       si
*      Total     cont   binary  integer     sos1     sos2    scont     sint
*          9        9        0        0        0        0        0        0
*  FX      0
*  
*  Nonzero counts
*      Total    const       NL      DLL
*         25        9       16        0
*
*  Solve m using NLP minimizing objvar;


Variables  x1,x2,x3,x4,x5,x6,x7,x8,objvar;

Equations  e1,e2,e3,e4,e5,e6,e7,e8,e9;


e1.. 0.004731*x1*x3 - 0.1238*x1 - 0.3578*x2*x3 - 0.001637*x2 - 0.9338*x4 + x7
      =E= 0.3571;

e2.. 0.2238*x1*x3 + 0.2638*x1 + 0.7623*x2*x3 - 0.07745*x2 - 0.6734*x4 - x7
      =E= 0.6022;

e3.. x6*x8 + 0.3578*x1 + 0.004731*x2 =E= 0;

e4..  - 0.7623*x1 + 0.2238*x2 =E= -0.3461;

e5.. sqr(x1) + sqr(x2) =E= 1;

e6.. sqr(x3) + sqr(x4) =E= 1;

e7.. sqr(x5) + sqr(x6) =E= 1;

e8.. sqr(x7) + sqr(x8) =E= 1;

e9..    objvar =E= 0;

* set non-default bounds
x1.lo = -1; x1.up = 1;
x2.lo = -1; x2.up = 1;
x3.lo = -1; x3.up = 1;
x4.lo = -1; x4.up = 1;
x5.lo = -1; x5.up = 1;
x6.lo = -1; x6.up = 1;
x7.lo = -1; x7.up = 1;
x8.lo = -1; x8.up = 1;

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: 2022-08-11 Git hash: f176bd52
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