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opt10_run.m
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opt10_run.m
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%% OPT10_RUN
%
% Modified:
%
% 08 January 2008
%
%---------------------------------------------------------------------
% Running the Wood Function
% This is used to test globalization methods.
%---------------------------------------------------------------------
fprintf('---------------------------------------------------------\n')
fprintf('Running testcase_10: exact solution (1, 1, 1, 1)\n')
fprintf('---------------------------------------------------------\n')
fname = 'opt10_fgh';
options = [];
options.verbose = 0;
options.method = 'newton';
options.max_iterations = 2000;
options.max_fevals = 2000;
fprintf('Newton:\n')
options.globalization = 'line_search';
x0 = [-3; -1; -3; -1 ];
x = entrust(fname, x0, options);
fprintf('Line search produced (%10.7e,%10.7e,%10.7e,%10.7e)\n\n',...
x(1),x(2),x(3),x(4))
f = opt10_fgh ( x, 'f' );
fprintf('Value of F(X) = %10.7e\n\n', f );
fprintf('Newton:\n')
options.globalization = 'trust_region';
x = entrust(fname, x0, options);
fprintf('Trust-region produced (%10.7e,%10.7e,%10.7e,%10.7e)\n\n',...
x(1),x(2),x(3),x(4))
f = opt10_fgh ( x, 'f' );
fprintf('Value of F(X) = %10.7e\n\n', f );
fprintf('Secant:\n')
options.method = 'secant';
options.initial_hessian = eye(4);
options.globalization = 'trust_region';
x = entrust(fname, x0, options);
fprintf('Trust-region produced (%10.7e,%10.7e,%10.7e,%10.7e)\n\n',...
x(1),x(2),x(3),x(4))
f = opt10_fgh ( x, 'f' );
fprintf('Value of F(X) = %10.7e\n\n', f );
%---------------------------------------------------------------------
% Test Gauss-Newton strategies.
%---------------------------------------------------------------------
fprintf('---------------------------------------------------------\n')
fprintf('Running testcase_10 as least squares problem: \n')
fprintf('Exact solution (1,1,1,1)\n')
fprintf('---------------------------------------------------------\n')
fname = 'opt10_rj';
options = [];
options.verbose = 0;
options.method = 'gauss_newton';
options.step_tolerance = 1.e-15;
options.globalization = 'none';
options.gradient_tolerance = 1.e-10;
options.max_iterations = 2000;
options.max_fevals = 2000;
x0 = [-3; -1; -3; -1 ];
x = entrust(fname, x0, options);
fprintf('Gauss-Newton produced (%8.5e,%8.5e,%8.5e,%8.5e)\n\n',...
x(1),x(2),x(3),x(4))
[ res, jac ] = opt10_rj ( x, 'f' );
fprintf('Norm of RES(X) = %10.7e\n\n', norm ( res ) );