264 lines
10 KiB
Prolog
264 lines
10 KiB
Prolog
%%% -*- Mode: Prolog; -*-
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%% @file lbfgs.pl
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% This file is part of YAP-LBFGS.
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% Copyright (C) 2009 Bernd Gutmann
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%
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% YAP-LBFGS is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% YAP-LBFGS is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with YAP-LBFGS. If not, see <http://www.gnu.org/licenses/>.
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:- module(lbfgs,[optimizer_initialize/3,
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optimizer_run/4,
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optimizer_finalize/1,
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optimizer_set_parameter/3,
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optimizer_get_parameter/3,
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optimizer_parameters/1]).
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% switch on all the checks to reduce bug searching time
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% :- yap_flag(unknown,error).
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% :- style_check(single_var).
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/**
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@defgroup YAP-LBFGS Interface to LibLBFGS
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@ingroup packages
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@short What is YAP-LBFGS? YAP-LBFGS is an interface to call [libLBFG](http://www.chokkan.org/software/liblbfgs/), from within
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YAP. libLBFGS is a C library for Limited-memory
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Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) solving the under-constrained
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minimization problem:
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~~~~~~~~~~~~~~~~~~~~~~~~
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+ minimize `F(X), X=(x1,x2,..., xN)`
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~~~~~~~~~~~~~~~~~~~~~~~~
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### Contact
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YAP-LBFGS has been developed by Bernd Gutmann. In case you publish something using YAP-LBFGS, please give credit to me and to libLBFGS. And if you find YAP-LBFGS useful, or if you find a bug, or if you
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port it to another system, ... please send me an email.
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### License
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+ YAP-LBFGS is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, either version 3 of the License, or
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(at your option) any later version.
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+ YAP-LBFGS is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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### Usage</h2>
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The module lbfgs provides the following predicates after you loaded
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it by
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~~~~
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:-use_module(library(lbfgs)).
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~~~~
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+ use optimizer_set_paramater(Name,Value) to change parameters
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+ use optimizer_get_parameter(Name,Value) to see current parameters
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+ use optimizer_parameters to print this overview
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### Demo
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The following Prolog program, ex1.pl, searches for minimas of the
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function `f(x0)=sin(x0)`. In order to do so, it provides the
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call back predicate <span class="code">evaluate` which
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calculates `f(x0)` and the gradient `d/dx0 f=cos(x0)`.
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~~~~~
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:- use_module(lbfgs).
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% This is the call back function which evaluates F and the gradient of F
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evaluate(FX,_N,_Step) :-
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optimizer_get_x(0,X0),
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FX is sin(X0),
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G0 is cos(X0),
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optimizer_set_g(0,G0).
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% This is the call back function which is invoked to report the progress
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% if the last argument is set to anything else than 0, the optimizer will
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% stop right now
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progress(FX,X_Norm,G_Norm,Step,_N,Iteration,Ls,0) :-
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optimizer_get_x(0,X0),
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format('~d. Iteration : x0=~4f f(X)=~4f |X|=~4f
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|X\'|=~4f Step=~4f Ls=~4f~n',
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[Iteration,X0,FX,X_Norm,G_Norm,Step,Ls]).
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demo :-
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format('Optimizing the function f(x0) = sin(x0)~n',[]),
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optimizer_initialize(1,evaluate,progress),
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StartX is random*10,
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format('We start the search at the random position x0=~5f~2n',[StartX]),
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optimizer_set_x(0,StartX),
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optimizer_run(BestF,Status),
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optimizer_get_x(0,BestX0),
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optimizer_finalize,
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format('~2nOptimization done~nWe found a minimum at
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f(~f)=~f~2nLBFGS Status=~w~n',[BestX0,BestF,Status]).
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~~~~~
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The output of this program is something like:
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~~~~~
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?- demo.
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Optimizing the function f(x0) = sin(x0)
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We start the search at the random position x0=7.24639
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1. Iteration : x0=5.0167 f(X)=-0.9541 |X|=5.0167 |X'|=0.2996 Step=3.9057 Ls=3.0000
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2. Iteration : x0=4.7708 f(X)=-0.9983 |X|=4.7708 |X'|=0.0584 Step=0.0998 Ls=2.0000
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3. Iteration : x0=4.7113 f(X)=-1.0000 |X|=4.7113 |X'|=0.0011 Step=1.0000 Ls=1.0000
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4. Iteration : x0=4.7124 f(X)=-1.0000 |X|=4.7124 |X'|=0.0000 Step=1.0000 Ls=1.0000
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Optimization done
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We found a minimum at f(4.712390)=-1.000000
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LBFGS Status=0
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yes
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?-
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~~~~~
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@{
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*/
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:- dynamic initialized/0.
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:- load_foreign_files(['libLBFGS'],[],'init_lbfgs_predicates').
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/** @pred optimizer_initialize(+N,+Evaluate,+Progress)
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The same as before, except that the user module is the default
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value.
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Example
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~~~~
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optimizer_initialize(1)
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~~~~~
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*/
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optimizer_initialize(N,X,t(N,X,XO,Params)) :-
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integer(N),
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N>0,
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% check whether there are such call back functions
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optimizer_reserve_memory(N,X,XO,Params).
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% install call back predicates in the user module which call
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% the predicates given by the arguments
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/** @pred optimizer_finalize/0
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Clean up the memory.
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*/
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optimizer_finalize(t(N,X,XO,Params)) :-
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initialized,
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optimizer_free_memory(X,XO,Params) ,
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retractall(initialized).
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/** @pred optimizer_run/3
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Do the work.
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*/
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optimizer_run(t(N,X,XO,Params),FX,XO,Status) :-
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optimizer_run(N,X, FX, XO, Status, Params).
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/** @pred optimizer_parameters/1
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Prints a table with the current parameters. See the <a href="http://www.chokkan.org/software/liblbfgs/structlbfgs__parameter__t.html#_details">documentation
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of libLBFGS</a> for the meaning of each parameter.
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~~~~
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?- optimizer_parameters.
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==========================================================================================
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Type Name Value Description
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==========================================================================================
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int m 6 The number of corrections to approximate the inverse hessian matrix.
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float epsilon 1e-05 Epsilon for convergence test.
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int past 0 Distance for delta-based convergence test.
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float delta 1e-05 Delta for convergence test.
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int max_iterations 0 The maximum number of iterations
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int linesearch 0 The line search algorithm.
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int max_linesearch 40 The maximum number of trials for the line search.
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float min_step 1e-20 The minimum step of the line search routine.
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float max_step 1e+20 The maximum step of the line search.
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float ftol 0.0001 A parameter to control the accuracy of the line search routine.
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float gtol 0.9 A parameter to control the accuracy of the line search routine.
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float xtol 1e-16 The machine precision for floating-point values.
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float orthantwise_c 0.0 Coefficient for the L1 norm of variables
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int orthantwise_start 0 Start index for computing the L1 norm of the variables.
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int orthantwise_end -1 End index for computing the L1 norm of the variables.
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==========================================================================================
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~~~~
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*/
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optimizer_parameterse(t(X,XO,Params)) :-
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optimizer_get_parameter(m,M ,Params),
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optimizer_get_parameter(epsilon,Epsilon ,Params),
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optimizer_get_parameter(past,Past ,Params),
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optimizer_get_parameter(delta,Delta ,Params),
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optimizer_get_parameter(max_iterations,Max_Iterations ,Params),
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optimizer_get_parameter(linesearch,Linesearch ,Params),
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optimizer_get_parameter(max_linesearch,Max_Linesearch ,Params),
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optimizer_get_parameter(min_step,Min_Step ,Params),
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optimizer_get_parameter(max_step,Max_Step ,Params),
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optimizer_get_parameter(ftol,Ftol ,Params),
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optimizer_get_parameter(gtol,Gtol ,Params),
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optimizer_get_parameter(xtol,Xtol ,Params),
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optimizer_get_parameter(orthantwise_c,Orthantwise_C ,Params),
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optimizer_get_parameter(orthantwise_start,Orthantwise_Start ,Params),
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optimizer_get_parameter(orthantwise_end,Orthantwise_End ,Params),
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format('/******************************************************************************************~n',[] ),
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print_param('Name','Value','Description','Type' ,Params),
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format('******************************************************************************************~n',[] ),
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print_param(m,M,'The number of corrections to approximate the inverse hessian matrix.',int ,Params),
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print_param(epsilon,Epsilon,'Epsilon for convergence test.',float ,Params),
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print_param(past,Past,'Distance for delta-based convergence test.',int ,Params),
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print_param(delta,Delta,'Delta for convergence test.',float ,Params),
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print_param(max_iterations,Max_Iterations,'The maximum number of iterations',int ,Params),
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print_param(linesearch,Linesearch,'The line search algorithm.',int ,Params),
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print_param(max_linesearch,Max_Linesearch,'The maximum number of trials for the line search.',int ,Params),
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print_param(min_step,Min_Step,'The minimum step of the line search routine.',float ,Params),
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print_param(max_step,Max_Step,'The maximum step of the line search.',float ,Params),
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print_param(ftol,Ftol,'A parameter to control the accuracy of the line search routine.',float ,Params),
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print_param(gtol,Gtol,'A parameter to control the accuracy of the line search routine.',float ,Params),
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print_param(xtol,Xtol,'The machine precision for floating-point values.',float ,Params),
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print_param(orthantwise_c,Orthantwise_C,'Coefficient for the L1 norm of variables',float ,Params),
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print_param(orthantwise_start,Orthantwise_Start,'Start index for computing the L1 norm of the variables.',int ,Params),
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print_param(orthantwise_end,Orthantwise_End,'End index for computing the L1 norm of the variables.',int ,Params),
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format('******************************************************************************************/~n',[]),
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format(' use optimizer_set_paramater(Name,Value) to change parameters~n',[]),
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format(' use optimizer_get_parameter(Name,Value) to see current parameters~n',[]),
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format(' use optimizer_parameters to print this overview~2n',[]).
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print_param(Name,Value,Text,Dom) :-
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format(user,'~w~10+~w~19+~w~15+~w~30+~n',[Dom,Name,Value,Text]).
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%% @}
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