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// Example with objective function, equality, inequality constraints, variable bounds, non linear function and options
function y=fun(x)
y=x(1)*x(1)+x(2)*x(2);
endfunction
x0 = [1,2];
A=[1,1 ; 1,1/4 ; 1,-1 ; -1/4,-1 ; -1,-1 ; -1,1];
b=[2;1;2;1;-1;2];
Aeq = [1,3]
beq= [5]
lb = [0 0]
ub = [2 1.5]
function [c,ceq]=nlc(x)
c = [x(1)^2 - x(2)^2 + 0.5 , x(1)^2 + x(2)^2 - 2.5];
ceq = [];
endfunction
options = list("MaxIter", [1500], "CpuTime", [500])
//Output
//Optimal Solution Found.
// hessian =
//
// 3970695.7 3.311D-10
// 3.311D-10 3970695.5
// gradient =
//
// 1.0000000 3.
// lambda =
//
// lower: [1.818D-08,6.061D-09]
// upper: [6.061D-09,0.7272728]
// ineqlin: [0.3636363,7.273D-08,3.030D-09,3.463D-09,9.091D-09,9.091D-09]
// eqlin: -2.2698905
// ineqnonlin: [6.061D-09,0.9062541]
// eqnonlin: [0x0 constant]
// output =
//
// Iterations: 20
// Cpu_Time: 0.4
// Objective_Evaluation: 23
// Dual_Infeasibility: 5.171D-09
// exitflag =
//
// 0
// fopt =
//
// 2.5
// x0pt =
//
// 0.5000000
// 1.5
[x0pt,fopt,exitflag,gradient,hessian] = intfmincon(f,x0,intcon, A, b, Aeq, beq, lb, ub, nlc, options)
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