%%% -*- Mode: Prolog; -*-
% This file is part of YAP-LBFGS.
% Copyright (C) 2009 Bernd Gutmann
%
% YAP-LBFGS 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 3 of the License, or
% (at your option) any later version.
%
% YAP-LBFGS 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 YAP-LBFGS. If not, see .
:- module(lbfgs,[optimizer_initialize/3,
optimizer_initialize/4,
optimizer_run/2,
optimizer_get_x/2,
optimizer_set_x/2,
optimizer_get_g/2,
optimizer_set_g/2,
optimizer_finalize/0,
optimizer_set_parameter/2,
optimizer_get_parameter/2,
optimizer_parameters/0]).
% switch on all the checks to reduce bug searching time
% :- yap_flag(unknown,error).
% :- style_check(single_var).
/**
@defgroup YAP-LBFGS
@ingroup YAPPackages
@short What is YAP-LBFGS? YAP-LBFGS is an interface to call libLBFGS, http://www.chokkan.org/software/liblbfgs/, from within
YAP. libLBFGS is a C library for Limited-memory
Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) solving the under-constrained
minimization problem:
+ minimize `F(X), X=(x1,x2,..., xN)`
### Contact
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
port it to another system, ... please send me an email.
### License
+ YAP-LBFGS 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 3 of the License, or
(at your option) any later version.
+ YAP-LBFGS 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.
### Usage
The module lbfgs provides the following predicates after you loaded
it by
~~~~
:-use_module(library(lbfgs)).
~~~~
+ use optimizer_set_paramater(Name,Value) to change parameters
+ use optimizer_get_parameter(Name,Value) to see current parameters
+ use optimizer_parameters to print this overview
### Demo
The following Prolog program, ex1.pl, searches for minimas of the
function `f(x0)=sin(x0)`. In order to do so, it provides the
call back predicate evaluate` which
calculates `f(x0)` and the gradient `d/dx0 f=cos(x0)`.
~~~~~
:- use_module(lbfgs).
% This is the call back function which evaluates F and the gradient of F
evaluate(FX,_N,_Step) :-
optimizer_get_x(0,X0),
FX is sin(X0),
G0 is cos(X0),
optimizer_set_g(0,G0).
% This is the call back function which is invoked to report the progress
% if the last argument is set to anything else than 0, the optimizer will
% stop right now
progress(FX,X_Norm,G_Norm,Step,_N,Iteration,Ls,0) :-
optimizer_get_x(0,X0),
format('~d. Iteration : x0=~4f f(X)=~4f |X|=~4f
|X\'|=~4f Step=~4f Ls=~4f~n',
[Iteration,X0,FX,X_Norm,G_Norm,Step,Ls]).
demo :-
format('Optimizing the function f(x0) = sin(x0)~n',[]),
optimizer_initialize(1,evaluate,progress),
StartX is random*10,
format('We start the search at the random position x0=~5f~2n',[StartX]),
optimizer_set_x(0,StartX),
optimizer_run(BestF,Status),
optimizer_get_x(0,BestX0),
optimizer_finalize,
format('~2nOptimization done~nWe found a minimum at
f(~f)=~f~2nLBFGS Status=~w~n',[BestX0,BestF,Status]).
~~~~~
The output of this program is something like:
~~~~~
?- demo.
Optimizing the function f(x0) = sin(x0)
We start the search at the random position x0=7.24639
1. Iteration : x0=5.0167 f(X)=-0.9541 |X|=5.0167 |X'|=0.2996 Step=3.9057 Ls=3.0000
2. Iteration : x0=4.7708 f(X)=-0.9983 |X|=4.7708 |X'|=0.0584 Step=0.0998 Ls=2.0000
3. Iteration : x0=4.7113 f(X)=-1.0000 |X|=4.7113 |X'|=0.0011 Step=1.0000 Ls=1.0000
4. Iteration : x0=4.7124 f(X)=-1.0000 |X|=4.7124 |X'|=0.0000 Step=1.0000 Ls=1.0000
Optimization done
We found a minimum at f(4.712390)=-1.000000
LBFGS Status=0
yes
?-
~~~~~
@{
*/
:- dynamic initialized/0.
:- load_foreign_files(['yap_lbfgs'],[],'init_lbfgs_predicates').
/** @pred optimizer_initialize(+N,+Evaluate,+Progress)
The same as before, except that the user module is the default
value.
Example
~~~~
optimizer_initialize(1,evaluate,progress)
~~~~~
*/
optimizer_initialize(N,Call_Evaluate,Call_Progress) :-
optimizer_initialize(N,user,Call_Evaluate,Call_Progress).
optimizer_initialize(N,Module,Call_Evaluate,Call_Progress) :-
\+ initialized,
integer(N),
N>0,
% check whether there are such call back functions
current_module(Module),
current_predicate(Module:Call_Evaluate/3),
current_predicate(Module:Call_Progress/8),
optimizer_reserve_memory(N),
% install call back predicates in the user module which call
% the predicates given by the arguments
EvalGoal =.. [Call_Evaluate,E1,E2,E3],
ProgressGoal =.. [Call_Progress,P1,P2,P3,P4,P5,P6,P7,P8],
retractall( user:'$lbfgs_callback_evaluate'(_E1,_E2,_E3) ),
retractall( user:'$lbfgs_callback_progress'(_P1,_P2,_P3,_P4,_P5,_P6,_P7,_P8) ),
assert( (user:'$lbfgs_callback_evaluate'(E1,E2,E3) :- Module:EvalGoal, !) ),
assert( (user:'$lbfgs_callback_progress'(P1,P2,P3,P4,P5,P6,P7,P8) :- Module:ProgressGoal, !) ),
assert(initialized).
/** @pred optimizer_finalize/0
Clean up the memory.
*/
optimizer_finalize :-
initialized,
optimizer_free_memory,
retractall(user:'$lbfgs_callback_evaluate'(_,_,_)),
retractall(user:'$lbfgs_callback_progress'(_,_,_,_,_,_,_,_)),
retractall(initialized).
/** @pred optimizer_parameters/0
Prints a table with the current parameters. See the documentation
of libLBFGS for the meaning of each parameter.
~~~~
?- optimizer_parameters.
==========================================================================================
Type Name Value Description
==========================================================================================
int m 6 The number of corrections to approximate the inverse hessian matrix.
float epsilon 1e-05 Epsilon for convergence test.
int past 0 Distance for delta-based convergence test.
float delta 1e-05 Delta for convergence test.
int max_iterations 0 The maximum number of iterations
int linesearch 0 The line search algorithm.
int max_linesearch 40 The maximum number of trials for the line search.
float min_step 1e-20 The minimum step of the line search routine.
float max_step 1e+20 The maximum step of the line search.
float ftol 0.0001 A parameter to control the accuracy of the line search routine.
float gtol 0.9 A parameter to control the accuracy of the line search routine.
float xtol 1e-16 The machine precision for floating-point values.
float orthantwise_c 0.0 Coefficient for the L1 norm of variables
int orthantwise_start 0 Start index for computing the L1 norm of the variables.
int orthantwise_end -1 End index for computing the L1 norm of the variables.
==========================================================================================
~~~~
*/
optimizer_parameters :-
optimizer_get_parameter(m,M),
optimizer_get_parameter(epsilon,Epsilon),
optimizer_get_parameter(past,Past),
optimizer_get_parameter(delta,Delta),
optimizer_get_parameter(max_iterations,Max_Iterations),
optimizer_get_parameter(linesearch,Linesearch),
optimizer_get_parameter(max_linesearch,Max_Linesearch),
optimizer_get_parameter(min_step,Min_Step),
optimizer_get_parameter(max_step,Max_Step),
optimizer_get_parameter(ftol,Ftol),
optimizer_get_parameter(gtol,Gtol),
optimizer_get_parameter(xtol,Xtol),
optimizer_get_parameter(orthantwise_c,Orthantwise_C),
optimizer_get_parameter(orthantwise_start,Orthantwise_Start),
optimizer_get_parameter(orthantwise_end,Orthantwise_End),
format('/******************************************************************************************~n',[]),
print_param('Name','Value','Description','Type'),
format('******************************************************************************************~n',[]),
print_param(m,M,'The number of corrections to approximate the inverse hessian matrix.',int),
print_param(epsilon,Epsilon,'Epsilon for convergence test.',float),
print_param(past,Past,'Distance for delta-based convergence test.',int),
print_param(delta,Delta,'Delta for convergence test.',float),
print_param(max_iterations,Max_Iterations,'The maximum number of iterations',int),
print_param(linesearch,Linesearch,'The line search algorithm.',int),
print_param(max_linesearch,Max_Linesearch,'The maximum number of trials for the line search.',int),
print_param(min_step,Min_Step,'The minimum step of the line search routine.',float),
print_param(max_step,Max_Step,'The maximum step of the line search.',float),
print_param(ftol,Ftol,'A parameter to control the accuracy of the line search routine.',float),
print_param(gtol,Gtol,'A parameter to control the accuracy of the line search routine.',float),
print_param(xtol,Xtol,'The machine precision for floating-point values.',float),
print_param(orthantwise_c,Orthantwise_C,'Coefficient for the L1 norm of variables',float),
print_param(orthantwise_start,Orthantwise_Start,'Start index for computing the L1 norm of the variables.',int),
print_param(orthantwise_end,Orthantwise_End,'End index for computing the L1 norm of the variables.',int),
format('******************************************************************************************/~n',[]),
format(' use optimizer_set_paramater(Name,Value) to change parameters~n',[]),
format(' use optimizer_get_parameter(Name,Value) to see current parameters~n',[]),
format(' use optimizer_parameters to print this overview~2n',[]).
print_param(Name,Value,Text,Dom) :-
format(user,'~w~10+~w~19+~w~15+~w~30+~n',[Dom,Name,Value,Text]).