bandemo.m

数字通信第四版原书的例程

%BANDEMO Banana function minimization demonstration.
% 	
%   This script-file demonstrates the minimization of the 
%   the banana function:  
%    
%           f(x)= 100*(x(2)-x(1)^2)^2+(1-x(1))^2 
% 
%   It is called the banana function because of the way the  
%   curvature bends around the origin (also called Rosenbrock's 
%   function). It is notorious in optimization examples because 
%   of the slow convergence with which most methods exhibit 
%   when trying to solve this  problem. 
% 
%   This function has a unique minimum at the point X=[1,1] f(x)=0. 
%   We demonstrate here a number of techniques for its minimization 
%   starting at the point X=[-1.9,2]. 
% 

echo off
clc
help bandemo
disp(' Strike any key for a mesh plot of the banana function')
xx = [-2:0.125:2]';
yy = [-1:0.125:3]';
[x,y]=meshgrid(xx',yy') ;
meshd = 100.*(y-x.*x).^2 + (1-x).^2; 
pause
mesh(meshd)

disp('')
disp(' Strike any key for a contour plot of the banana function')
pause
clf
conts = exp(3:20);
contour(xx,yy,meshd,conts,'w--') 
xlabel('x1')
ylabel('x2')
title('Minimization of the Banana function')
drawnow; % Draws current graph now 
hold on
plot(-1.9,2,'o')
text(-1.9,2,'Start Point')
plot(1,1,'o')
text(1,1,'Solution')
disp('Please wait - compiling optimization routines')

% test_long is a variable used for auto testing of this routine
if ~exist('test_long')  test_long = 0; end
if exist('method')~= 1 method = 7; end
if ~length(method) method = 7; end
l = 2;

while 1
	if ~test_long
		clc
		disp(' ')
		disp('   Choose any of the following methods to minimize the banana function')
		disp('')        
		disp('        UNCONSTRAINED:    1) Broyden-Fletcher-Golfarb-Shanno')
		disp('                          2) Davidon-Fletcher-Powell')
		disp('                          3) Steepest Descent')
		disp('                          4) Simplex Search')
		disp('         LEAST SQUARES:   5) Gauss-Newton')
		disp('                          6) Levenberg-Marquardt ')
		disp('')
		disp('                          0) Quit')
	end

	if test_long
		if l>=2
		    method=method-1;
		    l = 0;
		end
	else
		method=-1;
	end
	while  (method < 0) | (method > 6) 
		method = [];
		while ~length(method) 
		    method = input('Select a method number: ');
		end
	end
	if (method == 0) 
		hold off
		return
	end
	OPTIONS=0;
	if method==2, OPTIONS(6)=1;
	elseif method==3, OPTIONS(6)=2;
	elseif method==5, OPTIONS(5)=1;
	end
	if test_long
		l = l + 1;
	else
		l = [];
	end
	if method~=4
		disp('')
		disp('    Choose any of the following line search methods')
		disp('')
		disp('           1) Mixed Polynomial Interpolation')
		disp('           2) Cubic Interpolation')
		disp('')
		while ~length(l)
		    l = input('Select a line search number: ');
		end
		if l==2, OPTIONS(7)=1; end
	end
	if method~=4
		OPTIONS(14)=200;
	else
		OPTIONS(14)=300;
	end
	x=[-1.9,2];
	disp(' ')
	if method<4
		GRAD='[100*(4*x(1)^3-4*x(1)*x(2))+2*x(1)-2; 100*(2*x(2)-2*x(1)^2); banplot(x,OLDX)]';
		f='100*(x(2)-x(1)^2)^2+(1-x(1))^2';
		disp('[x, options] = fminu(f,x,OPTIONS,GRAD);')
		[x, options] = fminu(f,x,OPTIONS,GRAD);
	elseif method==4
		f='[100*(x(2)-x(1)^2)^2+(1-x(1))^2; banplot2(x)]';
		disp('[x, options]=fmins(f,x,OPTIONS);')
		[x, options]=fmins(f,x,OPTIONS);
	else
		GRAD='[-20*x(1), -1; 10, 0; banplot(x,OLDX)]';
		f='[10*(x(2)-x(1)^2),(1-x(1))]';
		disp('[x,options]=leastsq(f,x,OPTIONS,GRAD);')
		[x,options]=leastsq(f,x,OPTIONS,GRAD);
	end
	if test_long
		if OPTIONS(8)-100*(method==3) > 1e-8, error('Optimization Toolbox in datdemo'), end
	end

	disp(sprintf('\nValue of the function at the solution: %g', options(8)) ); 
	disp(sprintf('Number of function evaluations: %g', options(10)) ); 
	disp(sprintf('Number of gradient evaluations: %g\n\n', options(11)) ); 
	disp('Strike any key for menu')
	pause
	clf
	hold off
        contour(xx,yy,meshd,conts,'w--') 
	xlabel('x1')
	ylabel('x2')
	title('Minimization of the Banana function')
	hold on
	plot(-1.9,2,'o')
	text(-1.9,2,'Start Point')
	plot(1,1,'o')
	text(1,1,'Solution')
end