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view main/optim/inst/bfgsmin.m @ 7870:b11b5363d680 octave-forge
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author | i7tiol |
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date | Sun, 16 Jan 2011 17:42:02 +0000 |
parents | cc2712cc6cb7 |
children | d30cfca46e8a |
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## Copyright (C) 2006 Michael Creel <michael.creel@uab.es> ## ## This program is free software; you can redistribute it and/or modify ## it under the terms of the GNU General Public License as published by ## the Free Software Foundation; either version 2 of the License, or ## (at your option) any later version. ## ## This program is distributed in the hope that it will be useful, ## but WITHOUT ANY WARRANTY; without even the implied warranty of ## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the ## GNU General Public License for more details. ## ## You should have received a copy of the GNU General Public License ## along with this program; if not, write to the Free Software ## Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA ## bfgsmin: bfgs or limited memory bfgs minimization of function ## ## Usage: [x, obj_value, convergence, iters] = bfgsmin(f, args, control) ## ## The function must be of the form ## [value, return_2,..., return_m] = f(arg_1, arg_2,..., arg_n) ## By default, minimization is w.r.t. arg_1, but it can be done ## w.r.t. any argument that is a vector. Numeric derivatives are ## used unless analytic derivatives are supplied. See bfgsmin_example.m ## for methods. ## ## Arguments: ## * f: name of function to minimize (string) ## * args: a cell array that holds all arguments of the function ## The argument with respect to which minimization is done ## MUST be a vector ## * control: an optional cell array of 1-8 elements. If a cell ## array shorter than 8 elements is provided, the trailing elements ## are provided with default values. ## * elem 1: maximum iterations (positive integer, or -1 or Inf for unlimited (default)) ## * elem 2: verbosity ## 0 = no screen output (default) ## 1 = only final results ## 2 = summary every iteration ## 3 = detailed information ## * elem 3: convergence criterion ## 1 = strict (function, gradient and param change) (default) ## 0 = weak - only function convergence required ## * elem 4: arg in f_args with respect to which minimization is done (default is first) ## * elem 5: (optional) Memory limit for lbfgs. If it's a positive integer ## then lbfgs will be use. Otherwise ordinary bfgs is used ## * elem 6: function change tolerance, default 1e-12 ## * elem 7: parameter change tolerance, default 1e-6 ## * elem 8: gradient tolerance, default 1e-5 ## ## Returns: ## * x: the minimizer ## * obj_value: the value of f() at x ## * convergence: 1 if normal conv, other values if not ## * iters: number of iterations performed ## ## Example: see bfgsmin_example.m function [parameter, obj, convergence, iters] = bfgsmin(f, f_args, control) # check number and types of arguments if ((nargin < 2) || (nargin > 3)) usage("bfgsmin: you must supply 2 or 3 arguments"); endif if (!ischar(f)) usage("bfgsmin: first argument must be string holding objective function name"); endif if (!iscell(f_args)) usage("bfgsmin: second argument must cell array of function arguments"); endif if (nargin > 2) if (!iscell(control)) usage("bfgsmin: 3rd argument must be a cell array of 1-8 elements"); endif if (length(control) > 8) usage("bfgsmin: 3rd argument must be a cell array of 1-8 elements"); endif else control = {}; endif # provide defaults for missing controls if (length(control) == 0) control{1} = -1; endif # limit on iterations if (length(control) == 1) control{2} = 0; endif # level of verbosity if (length(control) == 2) control{3} = 1; endif # strong (function, gradient and parameter change) convergence required? if (length(control) == 3) control{4} = 1; endif # argument with respect to which minimization is done if (length(control) == 4) control{5} = 0; endif # memory for lbfgs: 0 uses ordinary bfgs if (length(control) == 5) control{6} = 1e-10; endif # tolerance for function convergence if (length(control) == 6) control{7} = 1e-6; endif # tolerance for parameter convergence if (length(control) == 7) control{8} = 1e-5; endif # tolerance for gradient convergence # validity checks on all controls tmp = control{1}; if (((tmp !=Inf) && (tmp != -1)) && (tmp > 0 && (mod(tmp,1) != 0))) usage("bfgsmin: 1st element of 3rd argument (iteration limit) must be Inf or positive integer"); endif tmp = control{2}; if ((tmp < 0) || (tmp > 3) || (mod(tmp,1) != 0)) usage("bfgsmin: 2nd element of 3rd argument (verbosity level) must be 0, 1, 2, or 3"); endif tmp = control{3}; if ((tmp != 0) && (tmp != 1)) usage("bfgsmin: 3rd element of 3rd argument (strong/weak convergence) must be 0 (weak) or 1 (strong)"); endif tmp = control{4}; if ((tmp < 1) || (tmp > length(f_args)) || (mod(tmp,1) != 0)) usage("bfgsmin: 4th element of 3rd argument (arg with respect to which minimization is done) must be an integer that indicates one of the elements of f_args"); endif tmp = control{5}; if ((tmp < 0) || (mod(tmp,1) != 0)) usage("bfgsmin: 5th element of 3rd argument (memory for lbfgs must be zero (regular bfgs) or a positive integer"); endif tmp = control{6}; if (tmp < 0) usage("bfgsmin: 6th element of 3rd argument (tolerance for function convergence) must be a positive real number"); endif tmp = control{7}; if (tmp < 0) usage("bfgsmin: 7th element of 3rd argument (tolerance for parameter convergence) must be a positive real number"); endif tmp = control{8}; if (tmp < 0) usage("bfgsmin: 8th element of 3rd argument (tolerance for gradient convergence) must be a positive real number"); endif # check that the parameter we minimize w.r.t. is a vector minarg = control{4}; theta = f_args{minarg}; theta = theta(:); if (!isvector(theta)) usage("bfgsmin: minimization must be done with respect to a vector of parameters"); endif f_args{minarg} = theta; # now go ahead and do the minimization [parameter, obj, convergence, iters] = __bfgsmin(f, f_args, control); endfunction