jens
7b34529b24
git-svn-id: http://moon:8086/svn/matlab/trunk@91 801c6759-fa7c-4059-a304-17956f83a07c
1 line
1.2 KiB
Matlab
1 line
1.2 KiB
Matlab
function [f,g,H] = autoHess(x,type,funObj,varargin)
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% Numerically compute Hessian of objective function from gradient values
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p = length(x);
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if type == 1
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% Use finite differencing
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mu = 2*sqrt(1e-12)*(1+norm(x));
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[f,g] = funObj(x,varargin{:});
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diff = zeros(p);
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for j = 1:p
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e_j = zeros(p,1);
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e_j(j) = 1;
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[f diff(:,j)] = funObj(x + mu*e_j,varargin{:});
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end
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H = (diff-repmat(g,[1 p]))/mu;
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elseif type == 3 % Use Complex Differentials
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mu = 1e-150;
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diff = zeros(p);
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for j = 1:p
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e_j = zeros(p,1);
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e_j(j) = 1;
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[f(j) diff(:,j)] = funObj(x + mu*i*e_j,varargin{:});
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end
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f = mean(real(f));
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g = mean(real(diff),2);
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H = imag(diff)/mu;
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else % Use central differencing
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mu = 2*sqrt(1e-12)*(1+norm(x));
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f1 = zeros(p,1);
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f2 = zeros(p,1);
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diff1 = zeros(p);
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diff2 = zeros(p);
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for j = 1:p
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e_j = zeros(p,1);
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e_j(j) = 1;
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[f1(j) diff1(:,j)] = funObj(x + mu*e_j,varargin{:});
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[f2(j) diff2(:,j)] = funObj(x - mu*e_j,varargin{:});
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end
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f = mean([f1;f2]);
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g = mean([diff1 diff2],2);
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H = (diff1-diff2)/(2*mu);
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end
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% Make sure H is symmetric
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H = (H+H')/2;
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if 0 % DEBUG CODE
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[fReal gReal HReal] = funObj(x,varargin{:});
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[fReal f]
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[gReal g]
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[HReal H]
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pause;
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end |