jens
7b34529b24
git-svn-id: http://moon:8086/svn/matlab/trunk@91 801c6759-fa7c-4059-a304-17956f83a07c
1 line
1.3 KiB
Matlab
1 line
1.3 KiB
Matlab
function [f,g] = autoGrad(x,type,funObj,varargin)
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% [f,g] = autoGrad(x,useComplex,funObj,varargin)
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%
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% Numerically compute gradient of objective function from function values
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%
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% type =
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% 1 - forward-differencing (p+1 evaluations)
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% 2 - central-differencing (more accurate, but requires 2p evaluations)
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% 3 - complex-step derivative (most accurate and only requires p evaluations, but only works for certain objectives)
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p = length(x);
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if type == 1 % Use Finite Differencing
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f = funObj(x,varargin{:});
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mu = 2*sqrt(1e-12)*(1+norm(x));
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diff = zeros(p,1);
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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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diff(j,1) = funObj(x + mu*e_j,varargin{:});
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end
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g = (diff-f)/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,1);
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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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diff(j,1) = funObj(x + mu*i*e_j,varargin{:});
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end
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f = mean(real(diff));
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g = 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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diff1 = zeros(p,1);
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diff2 = zeros(p,1);
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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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diff1(j,1) = funObj(x + mu*e_j,varargin{:});
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diff2(j,1) = funObj(x - mu*e_j,varargin{:});
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end
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f = mean([diff1;diff2]);
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g = (diff1 - diff2)/(2*mu);
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end
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if 0 % DEBUG CODE
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[fReal gReal] = funObj(x,varargin{:});
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[fReal f]
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[gReal g]
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diff
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pause;
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end |