jens
7b34529b24
git-svn-id: http://moon:8086/svn/matlab/trunk@91 801c6759-fa7c-4059-a304-17956f83a07c
76 lines
2.4 KiB
Matlab
76 lines
2.4 KiB
Matlab
% Runs various limited-memory solvers on 2D rosenbrock function for 25
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% function evaluations
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maxFunEvals = 25;
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fprintf('Result after %d evaluations of limited-memory solvers on 2D rosenbrock:\n',maxFunEvals);
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fprintf('---------------------------------------\n');
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fprintf('x1 = %.4f, x2 = %.4f (starting point)\n',0,0);
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fprintf('x1 = %.4f, x2 = %.4f (optimal solution)\n',1,1);
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fprintf('---------------------------------------\n');
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if exist('minimize') == 2
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% Minimize.m - conjugate gradient method
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x = minimize([0 0]', 'rosenbrock', -maxFunEvals);
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fprintf('x1 = %.4f, x2 = %.4f (minimize.m by C. Rasmussen)\n',x(1),x(2));
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end
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options = [];
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options.display = 'none';
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options.maxFunEvals = maxFunEvals;
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% Steepest Descent
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options.Method = 'sd';
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x = minFunc(@rosenbrock,[0 0]',options);
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fprintf('x1 = %.4f, x2 = %.4f (minFunc with steepest descent)\n',x(1),x(2));
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% Cyclic Steepest Descent
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options.Method = 'csd';
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x = minFunc(@rosenbrock,[0 0]',options);
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fprintf('x1 = %.4f, x2 = %.4f (minFunc with cyclic steepest descent)\n',x(1),x(2));
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% Barzilai & Borwein
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options.Method = 'bb';
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options.bbType = 1;
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x = minFunc(@rosenbrock,[0 0]',options);
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fprintf('x1 = %.4f, x2 = %.4f (minFunc with spectral gradient descent)\n',x(1),x(2));
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% Hessian-Free Newton
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options.Method = 'newton0';
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x = minFunc(@rosenbrock,[0 0]',options);
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fprintf('x1 = %.4f, x2 = %.4f (minFunc with Hessian-free Newton)\n',x(1),x(2));
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% Hessian-Free Newton w/ L-BFGS preconditioner
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options.Method = 'pnewton0';
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x = minFunc(@rosenbrock,[0 0]',options);
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fprintf('x1 = %.4f, x2 = %.4f (minFunc with preconditioned Hessian-free Newton)\n',x(1),x(2));
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% Conjugate Gradient
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options.Method = 'cg';
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x = minFunc(@rosenbrock,[0 0]',options);
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fprintf('x1 = %.4f, x2 = %.4f (minFunc with conjugate gradient)\n',x(1),x(2));
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% Scaled conjugate Gradient
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options.Method = 'scg';
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x = minFunc(@rosenbrock,[0 0]',options);
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fprintf('x1 = %.4f, x2 = %.4f (minFunc with scaled conjugate gradient)\n',x(1),x(2));
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% Preconditioned Conjugate Gradient
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options.Method = 'pcg';
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x = minFunc(@rosenbrock,[0 0]',options);
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fprintf('x1 = %.4f, x2 = %.4f (minFunc with preconditioned conjugate gradient)\n',x(1),x(2));
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% Default: L-BFGS (default)
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options.Method = 'lbfgs';
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x = minFunc(@rosenbrock,[0 0]',options);
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fprintf('x1 = %.4f, x2 = %.4f (minFunc with limited-memory BFGS - default)\n',x(1),x(2));
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fprintf('---------------------------------------\n');
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