From d5a0869a7d1482b67f3fd42805948ee30a05eb1e Mon Sep 17 00:00:00 2001 From: Harpreet Date: Thu, 4 Feb 2016 15:36:02 +0530 Subject: Source files --- help/en_US/scilab_en_US_help/fminunc.html | 180 ------------------------------ 1 file changed, 180 deletions(-) delete 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 deleted file mode 100644 index 636ea68..0000000 --- a/help/en_US/scilab_en_US_help/fminunc.html +++ /dev/null @@ -1,180 +0,0 @@ -
- -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) -// Press ENTER 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) |