From a2d9c2bfd6eb83d1a494821176388eb312d08254 Mon Sep 17 00:00:00 2001 From: Harpreet Date: Mon, 25 Jan 2016 01:05:02 +0530 Subject: functions added --- help/en_US/scilab_en_US_help/fminunc.html | 178 ++++++++++++++++++++++++++++++ 1 file changed, 178 insertions(+) create mode 100644 help/en_US/scilab_en_US_help/fminunc.html (limited to 'help/en_US/scilab_en_US_help/fminunc.html') diff --git a/help/en_US/scilab_en_US_help/fminunc.html b/help/en_US/scilab_en_US_help/fminunc.html new file mode 100644 index 0000000..97ffdbf --- /dev/null +++ b/help/en_US/scilab_en_US_help/fminunc.html @@ -0,0 +1,178 @@ +
+ +Solves a multi-variable unconstrainted optimization problem
xopt = fminunc(f,x0) +xopt = fminunc(f,x0,options) +[xopt,fopt] = fminunc(.....) +[xopt,fopt,exitflag]= fminunc(.....) +[xopt,fopt,exitflag,output]= fminunc(.....) +[xopt,fopt,exitflag,output,gradient]=fminunc(.....) +[xopt,fopt,exitflag,output,gradient,hessian]=fminunc(.....)
a function, representing the objective function of the problem
a vector of doubles, containing the starting of variables.
a list, containing the option for user to specify. See below for details.
a vector of doubles, the computed solution of the optimization problem.
a scalar of double, the function value at x.
a scalar of integer, containing the flag which denotes the reason for termination of algorithm. See below for details.
a structure, containing the information about the optimization. See below for details.
a vector of doubles, containing the the gradient of the solution.
a matrix of doubles, containing the the hessian of the solution.
Search the minimum of an unconstrained optimization problem specified by : +Find the minimum of f(x) such that
+The routine calls Ipopt for solving the Un-constrained Optimization problem, Ipopt is a library written in C++.
+The options allows the user to set various parameters of the Optimization problem. +It should be defined as type "list" and contains the following fields. +
The exitflag allows to know the status of the optimization which is given back by Ipopt. +
For more details on exitflag see the ipopt documentation, go to http://www.coin-or.org/Ipopt/documentation/
+The output data structure contains detailed informations about the optimization process. +It has type "struct" and contains the following fields. +
//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) | ![]() | ![]() |
//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) | ![]() | ![]() |