yap-6.3/packages/yap-lbfgs/lbfgs.pl

%%% -*- 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 <http://www.gnu.org/licenses/>.



:- 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</h2>
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</h2>
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 <span class="code">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 <a href="http://www.chokkan.org/software/liblbfgs/structlbfgs__parameter__t.html#_details">documentation
of libLBFGS</a> 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]).