jens
7b34529b24
git-svn-id: http://moon:8086/svn/matlab/trunk@91 801c6759-fa7c-4059-a304-17956f83a07c
1 line
1.7 KiB
Matlab
1 line
1.7 KiB
Matlab
function diff = derivativeCheck(funObj,x,order,type,varargin)
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% diff = fastDerivativeCheck(funObj,x,order,varargin)
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if nargin < 3
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order = 1; % Only check gradient by default
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if nargin < 4
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type = 2; % Use central-differencing by default
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end
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end
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p = length(x);
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d = sign(randn(p,1));
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if order == 2
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fprintf('Checking Hessian-vector product along random direction:\n');
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[f,g,H] = funObj(x,varargin{:});
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Hv = H*d;
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if type == 1 % Use Finite Differencing
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mu = 2*sqrt(1e-12)*(1+norm(x))/(1+norm(x));
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[diff,diffa] = funObj(x+d*mu,varargin{:});
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Hv2 = (diffa-g)/mu;
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elseif type == 3 % Use Complex Differentials
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mu = 1e-150;
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[diff,diffa] = funObj(x+d*mu*i,varargin{:});
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Hv2 = imag(diffa-g)/mu;
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else % Use Central Differencing
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mu = 2*sqrt(1e-12)*(1+norm(x))/(1+norm(x));
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[diff1,diffa] = funObj(x+d*mu,varargin{:});
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[diff2,diffb] = funObj(x-d*mu,varargin{:});
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Hv2 = (diffa-diffb)/(2*mu);
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end
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fprintf('Max difference between user and numerical Hessian-vector product: %e\n',max(abs(Hv-Hv2)));
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else
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fprintf('Checking Gradient along random direction:\n');
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[f,g] = funObj(x,varargin{:});
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gtd = g'*d;
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if type == 1 % Use Finite Differencing
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mu = 2*sqrt(1e-12)*(1+norm(x))/(1+norm(x));
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diff = funObj(x+d*mu,varargin{:});
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gtd2 = (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,diffa] = funObj(x+d*mu*i,varargin{:});
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gtd2 = imag(diff)/mu;
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else % Use Central Differencing
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mu = 2*sqrt(1e-12)*(1+norm(x))/(1+norm(x));
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diff1 = funObj(x+d*mu,varargin{:});
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diff2 = funObj(x-d*mu,varargin{:});
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gtd2 = (diff1-diff2)/(2*mu);
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end
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fprintf('Max difference between user and numerical directional-derivative: %e\n',max(abs(gtd-gtd2)));
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end |