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+// Example where user provides gradient of the objective function
+
+function y=fun(x)
+y=x(1)*x(1)+x(2)*x(2);
+endfunction
+
+function y= fGrad(x)
+y= [2*x(1),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", [150], "CpuTime", [500], "GradObj", fGrad)
+
+//Output
+//Optimal Solution Found.
+// hessian =
+//
+// 3970695.6 3.311D-10
+// 3.311D-10 3970695.4
+// 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-09,0.9062542]
+// eqnonlin: [0x0 constant]
+// output =
+//
+// Iterations: 20
+// Cpu_Time: 0.852
+// Objective_Evaluation: 23
+// Dual_Infeasibility: 1.884D-09
+// exitflag =
+//
+// 0
+// fopt =
+//
+// 2.5
+// xopt =
+//
+// 0.5000000
+// 1.5
+
+[xopt,fopt,exitflag,output,lambda,gradient,hessian] = fmincon (fun, x0, A, b, Aeq, beq, lb, ub, nlc, options)