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https://github.com/c-sooyoung/fold_slice.git
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83 lines
3.4 KiB
Matlab
83 lines
3.4 KiB
Matlab
function [x, p] = cgmin1(func,x,itmax,ftol,xtol,varargin)
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% conjugate-gradient optimization routine
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% NOTE: linesearch subroutines do not use the gradient
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%
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% [x] = cgmin1(func,x,itmax,ftol,xtol,varargin)
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%
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% func = string name of objective function which returns both the
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% objective function value and the gradient
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% x = input as initial starting point and output as final point
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% itmax = maximum number of iterations (empty for default = 50)
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% ftol = relative function tolerance (empty for default = 1e-3)
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% xtol = absolute solution tolerance (empty for default = 1e-3)
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% varargin = extra variables required by objective function
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%
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% DISCLAIMER: This code is not intended for distribution. I have many
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% versions of this code and am constantly revising it. I believe this
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% version is working properly. However, I will not vouch for the code.
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% Anyone using the code for thesis research has a responsibility to go
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% through the code line-by-line and read relevant references to understand
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% the code completely. In my opinion, you have two options if you want to
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% publish results obtained with the code: (i) go through the code line-by-
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% line and read relevent references to understand how the code works and make
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% sure it is working properly for your application, or (ii) I can sit down
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% with you an go through this code and the additional code that you have
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% written to go along with it and make sure it is working properly. Option
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% (i) is preferred, and I ask that you do NOT acknowledge me in print (first,
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% it would be more appropriate for you to reference "Numerical Recipes",
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% and second, I prefer not to be named in a paper with which I do not have
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% detailed knowledge). If you decide to go with option (ii), I would expect
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% to learn the details of your research and be included in the author list.
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%
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% Sam Thurman, May 9, 2005
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import utils.*
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if isempty(itmax), itmax = 50; end
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if isempty(ftol), ftol = 1e-3; end
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if isempty(xtol), xtol = 1e-3; end
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% loop
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flg = 0; % use steepest descent for first iteration
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step = 0; % to guess at initial steplength
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for it = 1:itmax
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% function evaluation
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[f,grad,p] = feval(func,x,varargin{:});
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% disp(f)
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% check for feasibility
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if isinf(f), error('encountered an infeasible solution'), end
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if norm(grad(:))==0, return, end % done if gradient is zero (unlikely)
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% pick search direction
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if (flg==1) & (rem(it,25)~=0) % linesearch found a minimum -> use cg equations
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gg = g(:)'*g(:);
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% dgg = grad(:)'*grad(:); % this statement for Fletcher-Reeves
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dgg = (grad(:)+g(:))'*grad(:); % this statement for Polak-Ribiere
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ga = dgg/gg;
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g = -grad;
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h = g+ga*h;
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dx = h/norm(h(:));
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df = grad(:)'*dx(:);
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end
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if (flg==0) | (rem(it,25)==0) | (df>0) % revert to steepest decent
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g = -grad;
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h = g;
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dx = h/norm(h(:));
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df = grad(:)'*dx(:);
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end
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% initial steplength guess
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if step == 0
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step = max(0.001,min([1,2*abs(f/(grad(:)'*dx(:)))])); % same as fminusub.m (line 124) in optim toolbox
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else % oterwise use previous steplength
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step = step/10;
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end
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% linesearch
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[x,fvalue,step,flg] = engines.ML.linesearch(func,x,f,df,dx,step,varargin{:});
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% test for convergence
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if (2*abs(f-fvalue)<=ftol*(abs(f)+abs(fvalue)+ftol)) & (step*norm(dx(:))<=xtol) & (it~=1) % normal return
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return
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end
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end
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verbose(3, 'Maximum number of iterations exceeded.')
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return
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end
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