steep_desc3.m

BASIC MULTIDIMENSIONAL GRADIENT METHODS

% Program: steep_desc3.m
% Title: Steepest-descent algorithm
% Description: Implements the steepest-descent method
% (Algorithm 5.1) using the inexact line search 
% algorithm implemented in terms of Algorithm 4.6.
% Theory: See Practical Optimization Sec. 5.2.2.
% Input: 
%   fname: objective function
%   gname: gradient of the objective function
%      x0: initial point
%    epsi: optimization tolerance
% Output:   
%      xs: solution point
%      fs: objective function evaluated at xs.
%       k: number of iterations at convergence
% Example:
% Find the minimum of the Himmelblau function
%    f = (x1^2 + x2 - 11)^2 + (x1 + x2^2 - 7)^2
% using an initial point x0 = [6 6]' and termination
% tolerance epsi = 1e-6.
% Solution:
% Execute the command
%   [xs,fs,k] = steep_desc3('f_himm','g_himm',[6 6]',1e-6)
% Notes:
% 1. The program can be applied to any customized function
%    by defining the function of interest and its gradient 
%    in m-files.
% ========================================================
function [xs,fs,k] = steep_desc3(fname,gname,x0,epsi)
disp(' ')
disp('Program steep_desc3.m')
k = 1;
xk = x0;
gk = feval(gname,xk);
dk = -gk;
ak = inex_lsearch(xk,dk,fname,gname);
adk = ak*dk;
er = norm(adk);
while er >= epsi,
   xk = xk + adk;
   gk = feval(gname,xk);
   dk = -gk;
   ak = inex_lsearch(xk,dk,fname,gname);
   adk = ak*dk;
   er = norm(adk);
   k = k + 1;
end
format long
disp('Solution point:')
xs = xk + adk
disp('Objective function at the solution point:')
fs = feval(fname,xs)
format short
disp('Number of iterations performed:')
k