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+mode(1)
+//
+// Demo of intfminimax.sci
+//
+
+// A basic case :
+// we provide only the objective function and the nonlinear constraint
+// function
+function f = myfun(x)
+f(1)= 2*x(1)^2 + x(2)^2 - 48*x(1) - 40*x(2) + 304;     //Objectives
+f(2)= -x(1)^2 - 3*x(2)^2;
+f(3)= x(1) + 3*x(2) -18;
+f(4)= -x(1) - x(2);
+f(5)= x(1) + x(2) - 8;
+endfunction
+// The initial guess
+x0 = [0.1,0.1];
+// The expected solution : only 4 digits are guaranteed
+xopt = [4 4]
+fopt = [0 -64 -2 -8 0]
+intcon = [1]
+maxfopt = 0
+// Run fminimax
+[x,fval,maxfval,exitflag] = intfminimax(myfun, x0,intcon)
+// Press ENTER to continue
+halt()   // Press return to continue
+ 
+// A case where we provide the gradient of the objective
+// functions and the Jacobian matrix of the constraints.
+// The objective function and its gradient
+function [f,G] = myfun(x)
+f(1)= 2*x(1)^2 + x(2)^2 - 48*x(1) - 40*x(2) + 304;
+f(2)= -x(1)^2 - 3*x(2)^2;
+f(3)= x(1) + 3*x(2) -18;
+f(4)= -x(1) - x(2);
+f(5)= x(1) + x(2) - 8;
+G = [ 4*x(1) - 48, -2*x(1), 1, -1, 1;
+2*x(2) - 40, -6*x(2), 3, -1, 1; ]'
+endfunction
+// The nonlinear constraints
+function [c,ceq,DC,DCeq] = confun(x)
+// Inequality constraints
+c = [1.5 + x(1)*x(2) - x(1) - x(2), -x(1)*x(2) - 10]
+// No nonlinear equality constraints
+ceq=[]
+DC= [x(2)-1, -x(2);
+x(1)-1, -x(1)]'
+DCeq = []'
+endfunction
+// Test with both gradient of objective and gradient of constraints
+minimaxOptions = list("GradObj","on","GradCon","on");
+// The initial guess
+x0 = [0,10];
+intcon = [2]
+// Run intfminimax
+[x,fval,maxfval,exitflag] = intfminimax(myfun,x0,intcon,[],[],[],[],[],[], confun, minimaxOptions)
+//========= E N D === O F === D E M O =========//