mode(1) // // Demo of fminunc.sci // //Find x in R^2 such that it minimizes the Rosenbrock function //f = 100*(x2 - x1^2)^2 + (1-x1)^2 //Objective function to be minimised function y= f(x) y= 100*(x(2) - x(1)^2)^2 + (1-x(1))^2; endfunction //Starting point x0=[-1,2]; //Gradient of objective function function y= fGrad(x) y= [-400*x(1)*x(2) + 400*x(1)^3 + 2*x(1)-2, 200*(x(2)-x(1)^2)]; endfunction //Hessian of Objective Function function y= fHess(x) y= [1200*x(1)^2- 400*x(2) + 2, -400*x(1);-400*x(1), 200 ]; endfunction //Options options=list("MaxIter", [1500], "CpuTime", [500], "Gradient", fGrad, "Hessian", fHess); //Calling Ipopt [xopt,fopt,exitflag,output,gradient,hessian]=fminunc(f,x0,options) // Press ENTER to continue halt() // Press return to continue //Find x in R^2 such that the below function is minimum //f = x1^2 + x2^2 //Objective function to be minimised function y= f(x) y= x(1)^2 + x(2)^2; endfunction //Starting point x0=[2,1]; //Calling Ipopt [xopt,fopt]=fminunc(f,x0) // Press ENTER to continue halt() // Press return to continue //The below problem is an unbounded problem: //Find x in R^2 such that the below function is minimum //f = - x1^2 - x2^2 //Objective function to be minimised function y= f(x) y= -x(1)^2 - x(2)^2; endfunction //Starting point x0=[2,1]; //Gradient of objective function function y= fGrad(x) y= [-2*x(1),-2*x(2)]; endfunction //Hessian of Objective Function function y= fHess(x) y= [-2,0;0,-2]; endfunction //Options options=list("MaxIter", [1500], "CpuTime", [500], "Gradient", fGrad, "Hessian", fHess); //Calling Ipopt [xopt,fopt,exitflag,output,gradient,hessian]=fminunc(f,x0,options) //========= E N D === O F === D E M O =========//