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Bernd Gutmann's lbfgs interface

This commit is contained in:
Vitor Santos Costa 2013-06-13 17:57:55 -05:00
parent f1cbc7f3be
commit 7e1f3009e6
20 changed files with 20378 additions and 3047 deletions
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18463
configure vendored
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3
configure.in
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@ -2530,6 +2530,7 @@ mkdir -p packages/real
mkdir -p packages/semweb
mkdir -p packages/sgml
mkdir -p packages/xml
mkdir -p packages/yap-lbfgs
mkdir -p packages/zlib
mkdir -p packages/archive
mkdir -p swi
@ -2654,5 +2655,7 @@ AC_CONFIG_FILES([packages/prism/src/c/Makefile])
AC_CONFIG_FILES([packages/prism/src/prolog/Makefile])
fi
AC_CONFIG_FILES([packages/yap-lbfgs/Makefile])
AC_OUTPUT()

674
packages/yap-lbfgs/LICENSE.txt Normal file
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@ -0,0 +1,674 @@
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might be different; for a GUI interface, you would use an "about box".
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
<http://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
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#
# default base directory for YAP installation
# (EROOT for architecture-dependent files)
#
prefix = @prefix@
exec_prefix = @exec_prefix@
ROOTDIR = $(prefix)
EROOTDIR = @exec_prefix@
abs_top_builddir = @abs_top_builddir@
#
# where the binary should be
#
BINDIR = $(EROOTDIR)/bin
#
# where YAP should look for libraries
#
LIBDIR=@libdir@
SHAREDIR=$(ROOTDIR)/share/Yap
YAPLIBDIR=@libdir@/Yap
#
#
CC=@CC@
CFLAGS= @SHLIB_CFLAGS@ $(YAP_EXTRAS) $(DEFS) -I$(srcdir) -I../.. -I$(srcdir)/../../include -I$(srcdir)/liblbfgs-1.7/include
LDFLAGS=@LDFLAGS@
#
#
# You shouldn't need to change what follows.
#
INSTALL=@INSTALL@
INSTALL_DATA=@INSTALL_DATA@
INSTALL_PROGRAM=@INSTALL_PROGRAM@
SHELL=/bin/sh
RANLIB=@RANLIB@
srcdir=@srcdir@
SO=@SO@
#4.1VPATH=@srcdir@:@srcdir@/OPTYap
CWD=$(PWD)
#
OBJS=yap_lbfgs.o lbfgs.o
SOBJS=yap_lbfgs.@SO@
PLLIB=$(srcdir)/lbfgs.pl
#in some systems we just create a single object, in others we need to
# create a libray
all: $(SOBJS)
yap_lbfgs.o: $(srcdir)/yap_lbfgs.c
$(CC) -c $(CFLAGS) $(srcdir)/yap_lbfgs.c -o yap_lbfgs.o
lbfgs.o: $(srcdir)/liblbfgs-1.7/lib/lbfgs.c
$(CC) -c $(CFLAGS) -I $(srcdir)/liblbfgs-1.7/lib $(srcdir)/liblbfgs-1.7/lib/lbfgs.c -o lbfgs.o
@DO_SECOND_LD@yap_lbfgs.@SO@: yap_lbfgs.o lbfgs.o
@DO_SECOND_LD@ @SHLIB_LD@ $(LDFLAGS) -o yap_lbfgs.@SO@ yap_lbfgs.o lbfgs.o @EXTRA_LIBS_FOR_DLLS@
install: all
$(INSTALL_PROGRAM) $(SOBJS) $(DESTDIR)$(YAPLIBDIR)
for h in $(PLLIB); do $(INSTALL_DATA) $$h $(DESTDIR)$(SHAREDIR); done
clean:
rm -f *.o *~ $(OBJS) $(SOBJS) *.BAK

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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN"
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<title>YAP-LBFGS</title>
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<h1>YAP-LBFGS</h1>
<h2>What is YAP-LBFGS</h2>
<p>YAP-LBFGS is an interface to call <a href="http://www.chokkan.org/software/liblbfgs/">libLBFGS</a> from within
YAP. libLBFGS is a C library for Limited-memory
Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) solving the under-constrained
minimization problem:</p>
<p> minimize <span class="math">F(X), X=(x1,x2,..., xN)</span></p>
<p><a href="http://www.dcc.fc.up.pt/~vsc/Yap/">YAP</a> (Yet Another
Prolog) is a Prolog interpreter</p>
<h2>Contact</h2>
<p>YAP-LBFGS has been developed by Bernd Gutmann (<a
href="http://www.cs.kuleuven.be/cgi-bin/e-post.pl?epost=bernd.gutmann">email</a>,
<a href="http://www.cs.kuleuven.be/~bernd/">homepage</a>). 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.</p>
<h2>Download</h2>
<p>Latest version (25.04.2009) <a href="yap-lbfgs.tgz">yap-lbfgs.tgz</a></p>
<h2>Installation</h2>
<p>Note: The Make file is currently only working under Mac OS, but with little
effort it is possible to adapt it to your needs.<br />
And in case you are a C expert and can produce a better/more general Make file, please let me know.</p>
<ol>
<li>Download and compile <a
href="http://www.chokkan.org/software/liblbfgs/">libLBFGS</a>.</li>
<li>Download and unpack YAP-LBFGS</li>
<li>Edit the Makefile of
YAP-LBFGS and change the line <span class="code">LIBS= ../lib/lbfgs.o</span> to wherever the
lbfgs.o file can be found.</li>
<li>Execute Make in the YAP-LBFGS folder</li>
<li>Run <span class="code">yap -l ex1.pl</span> and run the query
<span class="code">:-demo.</span> to see whether everything works fine.</li>
</ol>
<h2>License</h2>
<p> 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.</p>
<p> 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.</p>
<h2>Usage</h2>
<p>The module lbfgs provides the following predicates after you loaded
it by <span class="code">:-use_module(lbfgs).</span></p>
<h3>optimizer_initialize(+N,+Module,+Evaluate,+Progress)</h3>
<p>Create space to optimize a function with N variables (N has to be
integer). <span class="code">Module</span> is the name of the module where the call back
predicates can be found, <span class="code">Evaluate</span> is the call back predicate (arity 3)
to evaluate the function math <span class="math">F</span>, <span class="code">Progress</span> is the call back predicate invoked
(arity 8) after every iteration</p>
<p>Example <span class="code">
optimizer_initialize(1,user,evaluate,progress)</span></p>
<p>The evaluate call back predicate has to be of the type <span class="code">evaluate(-F,+N,+Step)</span>. It has to calculate the current function value <span class="code">F</span>. <span class="code">N</span> is the size of the parameter vector (the value which was used to initialize LBFGS) and <span class="code">Step</span> is the current state of the line search. The call back predicate can access the current values of <span class="math">x[i]</span> by calling <span class="code">optimizer_get_x(+I,-Xi)</span>. Finally, the call back predicate has to calculate the gradient of <span class="math">F</span> and set its value by calling <span class="code">optimizer_set_g(+I,+Gi)</span> for every <span class="math">1&lt;=I&lt;=N</span>.</p>
<p>The progress call back predicate has to be of the type <span class="code">progress(+F,+X_Norm,+G_Norm,+Step,+N,+Iteration,+LS,-Continue)</span>. It is called after every iteration. The call back predicate can access the current values of X and of the gradient by calling <span class="code">optimizer_get_x(+I,-Xi)</span> and <span class="code">optimizer_get_g(+I,-Gi)</span> respectively. Howver, it must not call the setter predicates for <span class="code">X</span> or <span class="code">G</span>. If it tries to do so, the optimizer will terminate with an error. If <span class="code">Continue</span> is set to 0 (int) the optimization process will continue for one more iteration, any other value will terminate the optimization process.</p>
<h3>optimizer_initialize(+N,+Evaluate,+Progress)</h3>
<p>The same as before, except that the user module is the default
value.</p>
<p>Example <span class="code">
optimizer_initialize(1,evaluate,progress)</span></p>
<h3>optimizer_run(-F,-Status)</h3>
<p>Runs the optimization, <span class="code">F</span> is the best (minimal) function value and
Status (int) is the status code returned by libLBFGS. Anything except
0 indicates an error, see the <a href="http://www.chokkan.org/software/liblbfgs/group__liblbfgs__api.html#g06fc87d81c62e9abb8790b6e5713c55b">documentation of libLBFGS</a> for the
meaning.</p>
<h3>optimizer_get_x(+I,-X)</h3>
<p>Get the current value for <span class="math">x[I]</span>. Only possible when the optimizer is
initialized or running.</p>
<h3>optimizer_set_x(+I,+X)</h3>
<p>Set the current value for <span class="math">x[I]</span>. Only possible when the optimizer is
initialized but not running.</p>
<h3>optimizer_get_g(+I,-G)</h3>
<p>Get the current value for <span class="math">g[I]</span> (the partial derivative of <span class="math">F</span> with respect to <span class="math">x[I]</span>). Only possible when the optimizer is
initialized or running.</p>
<h3>optimizer_set_g(+I,+G)</h3>
<p>Set the current value for <span class="math">g[I]</span> (the partial derivative of <span class="math">F</span> with
respect to <span class="math">x[I]</span>). Can only be called from the evaluate call back predicate.</p>
<h3> optimizer_finalize/0</h3>
<p>Clean up the memory.</p>
<h3> optimizer_parameters/0</h3>
<p>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.</p>
<pre>
?- 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.
==========================================================================================
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
</pre>
<h3> optimizer_set_parameter(+Name,+Value)</h3>
<p>Set the parameter Name to Value. Only possible while the optimizer
is not running.</p>
<h3> optimizer_get_parameter(+Name,-Value)</h3>
<p>Get the current Value for Name</p>
<h2>Demo</h2>
<p>The following Prolog program (<span class="code">ex1.pl</span>) searches for minimas of the function
<span class="math">f(x0)=sin(x0)</span>. In order to do so, it provides the call back
predicate <span class="code">evaluate</span> which calculates <span class="math">f(x0)</span> and the gradient <span class="math">d/dx0 f=cos(x0)</span>.</p>
<pre>
:- 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]).
</pre>
<p>The output of this program is something like:</p>
<pre>
?- 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
?-
</pre>
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%%% -*- 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/>.
:- use_module(library(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 anywhting 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]).

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%%% -*- 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/>.
:- use_module(library(lbfgs)).
% This is the call back function which evaluates F and the gradient of F
evaluate(FX,_N,_Step) :-
optimizer_get_x(0,X0),
optimizer_get_x(1,X1),
FX is (X0-2)*(X0-2) + (X1-1)*(X1-1),
G0 is 2*(X0-2),
G1 is 2*(X1-1),
optimizer_set_g(0,G0),
optimizer_set_g(1,G1).
% This is the call back function which is invoked to report the progress
% if the last argument is set to anywhting 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),
optimizer_get_x(1,X1),
format('~d. Iteration : (x0,x1)=(~4f,~4f) f(X)=~4f |X|=~4f |X\'|=~4f Step=~4f Ls=~4f~n',[Iteration,X0,X1,FX,X_Norm,G_Norm,Step,Ls]).
demo :-
format('Optimizing the function f(x0,x1) = (x0-2)^2 + (x1-1)^2~n',[]),
optimizer_initialize(2,evaluate,progress),
StartX0 is random*1000-500,
StartX1 is random*1000-500,
format('We start the search at the random position (x0,x1)=(~5f,~5f)~2n',[StartX0,StartX1]),
optimizer_set_x(0,StartX0),
optimizer_set_x(1,StartX1),
optimizer_run(BestF,Status),
optimizer_get_x(0,BestX0),
optimizer_get_x(1,BestX1),
optimizer_finalize,
format('~2nOptimization done~nWe found a minimum at f(~f,~f)=~f~2nLBFGS Status=~w~n',[BestX0,BestX1,BestF,Status]).

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%%% -*- 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).
:- dynamic initialized/0.
:- dynamic user:'$lbfgs_callback_evaluate'/3.
:- dynamic user:'$lbfgs_callback_progress'/8.
:- load_foreign_files(['yap_lbfgs'],[],'init_lbfgs_predicates').
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],
assert( (user:'$lbfgs_callback_evaluate'(E1,E2,E3) :- once(call(Module:EvalGoal))) ),
assert( (user:'$lbfgs_callback_progress'(P1,P2,P3,P4,P5,P6,P7,P8) :- once(call(Module:ProgressGoal))) ),
assert(initialized).
optimizer_finalize :-
initialized,
optimizer_free_memory,
retractall(user:'$lbfgs_callback_evaluate'(_,_,_)),
retractall(user:'$lbfgs_callback_progress'(_,_,_,_,_,_,_,_)),
retractall(initialized).
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]).

1
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Naoaki Okazaki <okazaki at chokkan org>

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The MIT License
Copyright (c) 1990 Jorge Nocedal
Copyright (c) 2007-2009 Naoaki Okazaki
Permission is hereby granted, free of charge, to any person obtaining a
copy of this software and associated documentation files (the "Software"),
to deal in the Software without restriction, including without limitation
the rights to use, copy, modify, merge, publish, distribute, sublicense,
and/or sell copies of the Software, and to permit persons to whom the
Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.

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2009-02-28 Naoaki Okazaki <okazaki at chokkan org>
* libLBFGS 1.7:
- Improved OWL-QN routines for stability.
- Removed the support of OWL-QN method in MoreThuente algorithm
because it accidentally fails in early stages of iterations for some
objectives. Because of this change, the OW-LQN method must be used
with the backtracking algorithm (LBFGS_LINESEARCH_BACKTRACKING), or
the library returns LBFGSERR_INVALID_LINESEARCH.
- Renamed line search algorithms as follows:
- LBFGS_LINESEARCH_BACKTRACKING: regular Wolfe condition.
- LBFGS_LINESEARCH_BACKTRACKING_LOOSE: regular Wolfe condition.
- LBFGS_LINESEARCH_BACKTRACKING_STRONG: strong Wolfe condition.
- Source code clean-up.
2008-11-02 Naoaki Okazaki <okazaki at chokkan org>
* libLBFGS 1.6:
- Improved line-search algorithm with strong Wolfe condition, which
was contributed by Takashi Imamichi. This routine is now default for
LBFGS_LINESEARCH_BACKTRACKING. The previous line search algorithm
with regular Wolfe condition is still available as
LBFGS_LINESEARCH_BACKTRACKING_LOOSE.
- Configurable stop index for L1-norm computation. A member variable
lbfgs_parameter_t::orthantwise_end was added to specify the index
number at which the library stops computing the L1 norm of the
variables. This is useful to prevent some variables from being
regularized by the OW-LQN method.
- A sample program written in C++ (sample/sample.cpp).
2008-07-10 Naoaki Okazaki <okazaki at chokkan org>
* libLBFGS 1.5:
- Configurable starting index for L1-norm computation. A member
variable lbfgs_parameter_t::orthantwise_start was added to specify
the index number from which the library computes the L1 norm of the
variables.
- Fixed a zero-division error when the initial variables have already
been a minimizer (reported by Takashi Imamichi). In this case, the
library returns LBFGS_ALREADY_MINIMIZED status code.
- Defined LBFGS_SUCCESS status code as zero; removed unused constants,
LBFGSFALSE and LBFGSTRUE.
- Fixed a compile error in an implicit down-cast.
2008-04-25 Naoaki Okazaki <okazaki at chokkan org>
* libLBFGS 1.4:
- Configurable line search algorithms. A member variable
lbfgs_parameter_t::linesearch was added to choose either MoreThuente
method (LBFGS_LINESEARCH_MORETHUENTE) or backtracking algorithm
(LBFGS_LINESEARCH_BACKTRACKING).
- Fixed a bug: the previous version did not compute psuedo-gradients
properly in the line search routines for OW-LQN. This bug might quit
an iteration process too early when the OW-LQN routine was activated
(0 < lbfgs_parameter_t::orthantwise_c).
- Configure script for POSIX environments.
- SSE/SSE2 optimizations with GCC.
- New functions lbfgs_malloc and lbfgs_free to use SSE/SSE2 routines
transparently. It is uncessary to use these functions for libLBFGS
built without SSE/SSE2 routines; you can still use any memory
allocators if SSE/SSE2 routines are disabled in libLBFGS.
2007-12-16 Naoaki Okazaki <okazaki at chokkan org>
* libLBFGS 1.3:
- An API change. An argument was added to lbfgs() function to receive
the final value of the objective function. This argument can be set
to NULL if the final value is unnecessary.
- Fixed a null-pointer bug in the sample code (reported by Takashi
Imamichi).
- Added build scripts for Microsoft Visual Studio 2005 and GCC.
- Added README file.
2007-12-13 Naoaki Okazaki <okazaki at chokkan org>
* libLBFGS 1.2:
- Fixed a serious bug in orthant-wise L-BFGS. An important variable
was used without initialization.
- Configurable L-BFGS parameters (number of limited memories, epsilon).
2007-12-01 Naoaki Okazaki <okazaki at chokkan org>
* libLBFGS 1.1:
- Implemented orthant-wise L-BFGS.
- Implemented lbfgs_parameter_init() function.
- Fixed several bugs.
- API documentation.
2007-09-20 Naoaki Okazaki <okazaki at chokkan org>
* libLBFGS 1.0
- Initial release.

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Installation Instructions
*************************
Copyright (C) 1994, 1995, 1996, 1999, 2000, 2001, 2002, 2004 Free
Software Foundation, Inc.
This file is free documentation; the Free Software Foundation gives
unlimited permission to copy, distribute and modify it.
Basic Installation
==================
These are generic installation instructions.
The `configure' shell script attempts to guess correct values for
various system-dependent variables used during compilation. It uses
those values to create a `Makefile' in each directory of the package.
It may also create one or more `.h' files containing system-dependent
definitions. Finally, it creates a shell script `config.status' that
you can run in the future to recreate the current configuration, and a
file `config.log' containing compiler output (useful mainly for
debugging `configure').
It can also use an optional file (typically called `config.cache'
and enabled with `--cache-file=config.cache' or simply `-C') that saves
the results of its tests to speed up reconfiguring. (Caching is
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If you need to do unusual things to compile the package, please try
to figure out how `configure' could check whether to do them, and mail
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be considered for the next release. If you are using the cache, and at
some point `config.cache' contains results you don't want to keep, you
may remove or edit it.
The file `configure.ac' (or `configure.in') is used to create
`configure' by a program called `autoconf'. You only need
`configure.ac' if you want to change it or regenerate `configure' using
a newer version of `autoconf'.
The simplest way to compile this package is:
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`./configure' to configure the package for your system. If you're
using `csh' on an old version of System V, you might need to type
`sh ./configure' instead to prevent `csh' from trying to execute
`configure' itself.
Running `configure' takes awhile. While running, it prints some
messages telling which features it is checking for.
2. Type `make' to compile the package.
3. Optionally, type `make check' to run any self-tests that come with
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Compilers and Options
=====================
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You can give `configure' initial values for configuration parameters
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is an example:
./configure CC=c89 CFLAGS=-O2 LIBS=-lposix
*Note Defining Variables::, for more details.
Compiling For Multiple Architectures
====================================
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Installation Names
==================
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==========================
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================
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Defining Variables
==================
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environment passed to `configure'. However, some packages may run
configure again during the build, and the customized values of these
variables may be lost. In order to avoid this problem, you should set
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./configure CC=/usr/local2/bin/gcc
will cause the specified gcc to be used as the C compiler (unless it is
overridden in the site shell script).
`configure' Invocation
======================
`configure' recognizes the following options to control how it operates.
`--help'
`-h'
Print a summary of the options to `configure', and exit.
`--version'
`-V'
Print the version of Autoconf used to generate the `configure'
script, and exit.
`--cache-file=FILE'
Enable the cache: use and save the results of the tests in FILE,
traditionally `config.cache'. FILE defaults to `/dev/null' to
disable caching.
`--config-cache'
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Alias for `--cache-file=config.cache'.
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`--silent'
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Do not print messages saying which checks are being made. To
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Look for the package's source code in directory DIR. Usually
`configure' can determine that directory automatically.
`configure' also accepts some other, not widely useful, options. Run
`configure --help' for more details.

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libLBFGS: C library of limited-memory BFGS (L-BFGS)
Copyright (c) 1990, Jorge Nocedal
Copyright (c) 2007-2009, Naoaki Okazaki
=========================================================================
1. Introduction
=========================================================================
libLBFGS is a C port of the implementation of Limited-memory
Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) method written by Jorge Nocedal.
The original FORTRAN source code is available at:
http://www.ece.northwestern.edu/~nocedal/lbfgs.html
The L-BFGS method solves the unconstrainted minimization problem:
minimize F(x), x = (x1, x2, ..., xN),
only if the objective function F(x) and its gradient G(x) are computable.
Refer to the libLBFGS web site for more information.
http://www.chokkan.org/software/liblbfgs/
=========================================================================
2. How to build
=========================================================================
[Microsoft Visual Studio 2008]
Open the solution file "lbfgs.sln" and build it.
[GCC]
$ ./configure
$ make
$ make install # To install libLBFGS library and header.
=========================================================================
3. Note on SSE/SSE2 optimization
=========================================================================
This library has SSE/SSE2 optimization routines for vector arithmetic
operations on Intel/AMD processors. The SSE2 routine is for 64 bit double
values, and the SSE routine is for 32 bit float values. Since the default
parameters in libLBFGS are tuned for double precision values, it may need
to modify these parameters to use the SSE optimization routines.
To use the SSE2 optimization routine, specify --enable-sse2 option to the
configure script.
$ ./configure --enable-sse2
To build libLBFGS with SSE2 optimization enabled on Microsoft Visual
Studio 2005, define USE_SSE and __SSE2__ symbols.
Make sure to run libLBFGS on processors where SSE2 instrunctions are
available. The library does not check the existence of SSE2 instructions.
To package maintainers,
Please do not enable SSE/SSE2 optimization routine. The library built
with SSE/SSE2 optimization will crash without any notice when necessary
SSE/SSE2 instructions are unavailable on CPUs.
=========================================================================
4. License
=========================================================================
libLBFGS is distributed under the term of the MIT license.
Please refer to COPYING file in the distribution.
$Id: README 56 2009-02-28 09:41:21Z naoaki $

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/*
* C library of Limited memory BFGS (L-BFGS).
*
* Copyright (c) 1990, Jorge Nocedal
* Copyright (c) 2007,2008,2009 Naoaki Okazaki
* All rights reserved.
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
/* $Id: lbfgs.h 56 2009-02-28 09:41:21Z naoaki $ */
#ifndef __LBFGS_H__
#define __LBFGS_H__
#ifdef __cplusplus
extern "C" {
#endif/*__cplusplus*/
/*
* The default precision of floating point values is 64bit (double).
*/
#ifndef LBFGS_FLOAT
#define LBFGS_FLOAT 64
#endif/*LBFGS_FLOAT*/
/*
* Activate optimization routines for IEEE754 floating point values.
*/
#ifndef LBFGS_IEEE_FLOAT
#define LBFGS_IEEE_FLOAT 1
#endif/*LBFGS_IEEE_FLOAT*/
#if LBFGS_FLOAT == 32
typedef float lbfgsfloatval_t;
#elif LBFGS_FLOAT == 64
typedef double lbfgsfloatval_t;
#else
#error "libLBFGS supports single (float; LBFGS_FLOAT = 32) or double (double; LBFGS_FLOAT=64) precision only."
#endif
/**
* \addtogroup liblbfgs_api libLBFGS API
* @{
*
* The libLBFGS API.
*/
/**
* Return values of lbfgs().
*
* Roughly speaking, a negative value indicates an error.
*/
enum {
/** L-BFGS reaches convergence. */
LBFGS_SUCCESS = 0,
LBFGS_CONVERGENCE = 0,
LBFGS_STOP,
/** The initial variables already minimize the objective function. */
LBFGS_ALREADY_MINIMIZED,
/** Unknown error. */
LBFGSERR_UNKNOWNERROR = -1024,
/** Logic error. */
LBFGSERR_LOGICERROR,
/** Insufficient memory. */
LBFGSERR_OUTOFMEMORY,
/** The minimization process has been canceled. */
LBFGSERR_CANCELED,
/** Invalid number of variables specified. */
LBFGSERR_INVALID_N,
/** Invalid number of variables (for SSE) specified. */
LBFGSERR_INVALID_N_SSE,
/** The array x must be aligned to 16 (for SSE). */
LBFGSERR_INVALID_X_SSE,
/** Invalid parameter lbfgs_parameter_t::epsilon specified. */
LBFGSERR_INVALID_EPSILON,
/** Invalid parameter lbfgs_parameter_t::past specified. */
LBFGSERR_INVALID_TESTPERIOD,
/** Invalid parameter lbfgs_parameter_t::delta specified. */
LBFGSERR_INVALID_DELTA,
/** Invalid parameter lbfgs_parameter_t::linesearch specified. */
LBFGSERR_INVALID_LINESEARCH,
/** Invalid parameter lbfgs_parameter_t::max_step specified. */
LBFGSERR_INVALID_MINSTEP,
/** Invalid parameter lbfgs_parameter_t::max_step specified. */
LBFGSERR_INVALID_MAXSTEP,
/** Invalid parameter lbfgs_parameter_t::ftol specified. */
LBFGSERR_INVALID_FTOL,
/** Invalid parameter lbfgs_parameter_t::gtol specified. */
LBFGSERR_INVALID_GTOL,
/** Invalid parameter lbfgs_parameter_t::xtol specified. */
LBFGSERR_INVALID_XTOL,
/** Invalid parameter lbfgs_parameter_t::max_linesearch specified. */
LBFGSERR_INVALID_MAXLINESEARCH,
/** Invalid parameter lbfgs_parameter_t::orthantwise_c specified. */
LBFGSERR_INVALID_ORTHANTWISE,
/** Invalid parameter lbfgs_parameter_t::orthantwise_start specified. */
LBFGSERR_INVALID_ORTHANTWISE_START,
/** Invalid parameter lbfgs_parameter_t::orthantwise_end specified. */
LBFGSERR_INVALID_ORTHANTWISE_END,
/** The line-search step went out of the interval of uncertainty. */
LBFGSERR_OUTOFINTERVAL,
/** A logic error occurred; alternatively, the interval of uncertainty
became too small. */
LBFGSERR_INCORRECT_TMINMAX,
/** A rounding error occurred; alternatively, no line-search step
satisfies the sufficient decrease and curvature conditions. */
LBFGSERR_ROUNDING_ERROR,
/** The line-search step became smaller than lbfgs_parameter_t::min_step. */
LBFGSERR_MINIMUMSTEP,
/** The line-search step became larger than lbfgs_parameter_t::max_step. */
LBFGSERR_MAXIMUMSTEP,
/** The line-search routine reaches the maximum number of evaluations. */
LBFGSERR_MAXIMUMLINESEARCH,
/** The algorithm routine reaches the maximum number of iterations. */
LBFGSERR_MAXIMUMITERATION,
/** Relative width of the interval of uncertainty is at most
lbfgs_parameter_t::xtol. */
LBFGSERR_WIDTHTOOSMALL,
/** A logic error (negative line-search step) occurred. */
LBFGSERR_INVALIDPARAMETERS,
/** The current search direction increases the objective function value. */
LBFGSERR_INCREASEGRADIENT,
};
/**
* Line search algorithms.
*/
enum {
/** The default algorithm (MoreThuente method). */
LBFGS_LINESEARCH_DEFAULT = 0,
/** MoreThuente method proposd by More and Thuente. */
LBFGS_LINESEARCH_MORETHUENTE = 0,
/** Backtracking method with strong Wolfe condition. */
LBFGS_LINESEARCH_BACKTRACKING_STRONG = 1,
/** Backtracking method with regular Wolfe condition. */
LBFGS_LINESEARCH_BACKTRACKING = 2,
/** Backtracking method with regular Wolfe condition. */
LBFGS_LINESEARCH_BACKTRACKING_LOOSE = 2,
};
/**
* L-BFGS optimization parameters.
* Call lbfgs_parameter_init() function to initialize parameters to the
* default values.
*/
typedef struct {
/**
* The number of corrections to approximate the inverse hessian matrix.
* The L-BFGS routine stores the computation results of previous \ref m
* iterations to approximate the inverse hessian matrix of the current
* iteration. This parameter controls the size of the limited memories
* (corrections). The default value is \c 6. Values less than \c 3 are
* not recommended. Large values will result in excessive computing time.
*/
int m;
/**
* Epsilon for convergence test.
* This parameter determines the accuracy with which the solution is to
* be found. A minimization terminates when
* ||g|| < \ref epsilon * max(1, ||x||),
* where ||.|| denotes the Euclidean (L2) norm. The default value is
* \c 1e-5.
*/
lbfgsfloatval_t epsilon;
/**
* Distance for delta-based convergence test.
* This parameter determines the distance, in iterations, to compute
* the rate of decrease of the objective function. If the value of this
* parameter is zero, the library does not perform the delta-based
* convergence test. The default value is \c 0.
*/
int past;
/**
* Delta for convergence test.
* This parameter determines the minimum rate of decrease of the
* objective function. The library stops iterations when the
* following condition is met:
* (f' - f) / f < \ref delta,
* where f' is the objective value of \ref past iterations ago, and f is
* the objective value of the current iteration.
* The default value is \c 0.
*/
lbfgsfloatval_t delta;
/**
* The maximum number of iterations.
* The lbfgs() function terminates an optimization process with
* ::LBFGSERR_MAXIMUMITERATION status code when the iteration count
* exceedes this parameter. Setting this parameter to zero continues an
* optimization process until a convergence or error. The default value
* is \c 0.
*/
int max_iterations;
/**
* The line search algorithm.
* This parameter specifies a line search algorithm to be used by the
* L-BFGS routine.
*/
int linesearch;
/**
* The maximum number of trials for the line search.
* This parameter controls the number of function and gradients evaluations
* per iteration for the line search routine. The default value is \c 20.
*/
int max_linesearch;
/**
* The minimum step of the line search routine.
* The default value is \c 1e-20. This value need not be modified unless
* the exponents are too large for the machine being used, or unless the
* problem is extremely badly scaled (in which case the exponents should
* be increased).
*/
lbfgsfloatval_t min_step;
/**
* The maximum step of the line search.
* The default value is \c 1e+20. This value need not be modified unless
* the exponents are too large for the machine being used, or unless the
* problem is extremely badly scaled (in which case the exponents should
* be increased).
*/
lbfgsfloatval_t max_step;
/**
* A parameter to control the accuracy of the line search routine.
* The default value is \c 1e-4. This parameter should be greater
* than zero and smaller than \c 0.5.
*/
lbfgsfloatval_t ftol;
/**
* A parameter to control the accuracy of the line search routine.
* The default value is \c 0.9. If the function and gradient
* evaluations are inexpensive with respect to the cost of the
* iteration (which is sometimes the case when solving very large
* problems) it may be advantageous to set this parameter to a small
* value. A typical small value is \c 0.1. This parameter shuold be
* greater than the \ref ftol parameter (\c 1e-4) and smaller than
* \c 1.0.
*/
lbfgsfloatval_t gtol;
/**
* The machine precision for floating-point values.
* This parameter must be a positive value set by a client program to
* estimate the machine precision. The line search routine will terminate
* with the status code (::LBFGSERR_ROUNDING_ERROR) if the relative width
* of the interval of uncertainty is less than this parameter.
*/
lbfgsfloatval_t xtol;
/**
* Coeefficient for the L1 norm of variables.
* This parameter should be set to zero for standard minimization
* problems. Setting this parameter to a positive value activates
* Orthant-Wise Limited-memory Quasi-Newton (OWL-QN) method, which
* minimizes the objective function F(x) combined with the L1 norm |x|
* of the variables, {F(x) + C |x|}. This parameter is the coeefficient
* for the |x|, i.e., C. As the L1 norm |x| is not differentiable at
* zero, the library modifies function and gradient evaluations from
* a client program suitably; a client program thus have only to return
* the function value F(x) and gradients G(x) as usual. The default value
* is zero.
*/
lbfgsfloatval_t orthantwise_c;
/**
* Start index for computing L1 norm of the variables.
* This parameter is valid only for OWL-QN method
* (i.e., \ref orthantwise_c != 0). This parameter b (0 <= b < N)
* specifies the index number from which the library computes the
* L1 norm of the variables x,
* |x| := |x_{b}| + |x_{b+1}| + ... + |x_{N}| .
* In other words, variables x_1, ..., x_{b-1} are not used for
* computing the L1 norm. Setting b (0 < b < N), one can protect
* variables, x_1, ..., x_{b-1} (e.g., a bias term of logistic
* regression) from being regularized. The default value is zero.
*/
int orthantwise_start;
/**
* End index for computing L1 norm of the variables.
* This parameter is valid only for OWL-QN method
* (i.e., \ref orthantwise_c != 0). This parameter e (0 < e <= N)
* specifies the index number at which the library stops computing the
* L1 norm of the variables x,
*/
int orthantwise_end;
} lbfgs_parameter_t;
/**
* Callback interface to provide objective function and gradient evaluations.
*
* The lbfgs() function call this function to obtain the values of objective
* function and its gradients when needed. A client program must implement
* this function to evaluate the values of the objective function and its
* gradients, given current values of variables.
*
* @param instance The user data sent for lbfgs() function by the client.
* @param x The current values of variables.
* @param g The gradient vector. The callback function must compute
* the gradient values for the current variables.
* @param n The number of variables.
* @param step The current step of the line search routine.
* @retval lbfgsfloatval_t The value of the objective function for the current
* variables.
*/
typedef lbfgsfloatval_t (*lbfgs_evaluate_t)(
void *instance,
const lbfgsfloatval_t *x,
lbfgsfloatval_t *g,
const int n,
const lbfgsfloatval_t step
);
/**
* Callback interface to receive the progress of the optimization process.
*
* The lbfgs() function call this function for each iteration. Implementing
* this function, a client program can store or display the current progress
* of the optimization process.
*
* @param instance The user data sent for lbfgs() function by the client.
* @param x The current values of variables.
* @param g The current gradient values of variables.
* @param fx The current value of the objective function.
* @param xnorm The Euclidean norm of the variables.
* @param gnorm The Euclidean norm of the gradients.
* @param step The line-search step used for this iteration.
* @param n The number of variables.
* @param k The iteration count.
* @param ls The number of evaluations called for this iteration.
* @retval int Zero to continue the optimization process. Returning a
* non-zero value will cancel the optimization process.
*/
typedef int (*lbfgs_progress_t)(
void *instance,
const lbfgsfloatval_t *x,
const lbfgsfloatval_t *g,
const lbfgsfloatval_t fx,
const lbfgsfloatval_t xnorm,
const lbfgsfloatval_t gnorm,
const lbfgsfloatval_t step,
int n,
int k,
int ls
);
/*
A user must implement a function compatible with ::lbfgs_evaluate_t (evaluation
callback) and pass the pointer to the callback function to lbfgs() arguments.
Similarly, a user can implement a function compatible with ::lbfgs_progress_t
(progress callback) to obtain the current progress (e.g., variables, function
value, ||G||, etc) and to cancel the iteration process if necessary.
Implementation of a progress callback is optional: a user can pass \c NULL if
progress notification is not necessary.
In addition, a user must preserve two requirements:
- The number of variables must be multiples of 16 (this is not 4).
- The memory block of variable array ::x must be aligned to 16.
This algorithm terminates an optimization
when:
||G|| < \epsilon \cdot \max(1, ||x||) .
In this formula, ||.|| denotes the Euclidean norm.
*/
/**
* Start a L-BFGS optimization.
*
* @param n The number of variables.
* @param x The array of variables. A client program can set
* default values for the optimization and receive the
* optimization result through this array. This array
* must be allocated by ::lbfgs_malloc function
* for libLBFGS built with SSE/SSE2 optimization routine
* enabled. The library built without SSE/SSE2
* optimization does not have such a requirement.
* @param ptr_fx The pointer to the variable that receives the final
* value of the objective function for the variables.
* This argument can be set to \c NULL if the final
* value of the objective function is unnecessary.
* @param proc_evaluate The callback function to provide function and
* gradient evaluations given a current values of
* variables. A client program must implement a
* callback function compatible with \ref
* lbfgs_evaluate_t and pass the pointer to the
* callback function.
* @param proc_progress The callback function to receive the progress
* (the number of iterations, the current value of
* the objective function) of the minimization
* process. This argument can be set to \c NULL if
* a progress report is unnecessary.
* @param instance A user data for the client program. The callback
* functions will receive the value of this argument.
* @param param The pointer to a structure representing parameters for
* L-BFGS optimization. A client program can set this
* parameter to \c NULL to use the default parameters.
* Call lbfgs_parameter_init() function to fill a
* structure with the default values.
* @retval int The status code. This function returns zero if the
* minimization process terminates without an error. A
* non-zero value indicates an error.
*/
int lbfgs(
int n,
lbfgsfloatval_t *x,
lbfgsfloatval_t *ptr_fx,
lbfgs_evaluate_t proc_evaluate,
lbfgs_progress_t proc_progress,
void *instance,
lbfgs_parameter_t *param
);
/**
* Initialize L-BFGS parameters to the default values.
*
* Call this function to fill a parameter structure with the default values
* and overwrite parameter values if necessary.
*
* @param param The pointer to the parameter structure.
*/
void lbfgs_parameter_init(lbfgs_parameter_t *param);
/**
* Allocate an array for variables.
*
* This function allocates an array of variables for the convenience of
* ::lbfgs function; the function has a requreiemt for a variable array
* when libLBFGS is built with SSE/SSE2 optimization routines. A user does
* not have to use this function for libLBFGS built without SSE/SSE2
* optimization.
*
* @param n The number of variables.
*/
lbfgsfloatval_t* lbfgs_malloc(int n);
/**
* Free an array of variables.
*
* @param x The array of variables allocated by ::lbfgs_malloc
* function.
*/
void lbfgs_free(lbfgsfloatval_t *x);
/** @} */
#ifdef __cplusplus
}
#endif/*__cplusplus*/
/**
@mainpage libLBFGS: a library of Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS)
@section intro Introduction
This library is a C port of the implementation of Limited-memory
Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) method written by Jorge Nocedal.
The original FORTRAN source code is available at:
http://www.ece.northwestern.edu/~nocedal/lbfgs.html
The L-BFGS method solves the unconstrainted minimization problem,
<pre>
minimize F(x), x = (x1, x2, ..., xN),
</pre>
only if the objective function F(x) and its gradient G(x) are computable. The
well-known Newton's method requires computation of the inverse of the hessian
matrix of the objective function. However, the computational cost for the
inverse hessian matrix is expensive especially when the objective function
takes a large number of variables. The L-BFGS method iteratively finds a
minimizer by approximating the inverse hessian matrix by information from last
m iterations. This innovation saves the memory storage and computational time
drastically for large-scaled problems.
Among the various ports of L-BFGS, this library provides several features:
- <b>Optimization with L1-norm (Orthant-Wise Limited-memory Quasi-Newton
(OWL-QN) method)</b>:
In addition to standard minimization problems, the library can minimize
a function F(x) combined with L1-norm |x| of the variables,
{F(x) + C |x|}, where C is a constant scalar parameter. This feature is
useful for estimating parameters of sparse log-linear models (e.g.,
logistic regression and maximum entropy) with L1-regularization (or
Laplacian prior).
- <b>Clean C code</b>:
Unlike C codes generated automatically by f2c (Fortran 77 into C converter),
this port includes changes based on my interpretations, improvements,
optimizations, and clean-ups so that the ported code would be well-suited
for a C code. In addition to comments inherited from the original code,
a number of comments were added through my interpretations.
- <b>Callback interface</b>:
The library receives function and gradient values via a callback interface.
The library also notifies the progress of the optimization by invoking a
callback function. In the original implementation, a user had to set
function and gradient values every time the function returns for obtaining
updated values.
- <b>Thread safe</b>:
The library is thread-safe, which is the secondary gain from the callback
interface.
- <b>Cross platform.</b> The source code can be compiled on Microsoft Visual
Studio 2005, GNU C Compiler (gcc), etc.
- <b>Configurable precision</b>: A user can choose single-precision (float)
or double-precision (double) accuracy by changing ::LBFGS_FLOAT macro.
- <b>SSE/SSE2 optimization</b>:
This library includes SSE/SSE2 optimization (written in compiler intrinsics)
for vector arithmetic operations on Intel/AMD processors. The library uses
SSE for float values and SSE2 for double values. The SSE/SSE2 optimization
routine is disabled by default.
This library is used by:
- <a href="http://www.chokkan.org/software/crfsuite/">CRFsuite: A fast implementation of Conditional Random Fields (CRFs)</a>
- <a href="http://www.public.iastate.edu/~gdancik/mlegp/">mlegp: an R package for maximum likelihood estimates for Gaussian processes</a>
- <a href="http://infmath.uibk.ac.at/~matthiasf/imaging2/">imaging2: the imaging2 class library</a>
- <a href="http://search.cpan.org/~laye/Algorithm-LBFGS-0.16/">Algorithm::LBFGS - Perl extension for L-BFGS</a>
@section download Download
- <a href="http://www.chokkan.org/software/dist/liblbfgs-1.7.tar.gz">Source code</a>
libLBFGS is distributed under the term of the
<a href="http://opensource.org/licenses/mit-license.php">MIT license</a>.
@section changelog History
- Version 1.7 (2009-02-28):
- Improved OWL-QN routines for stability.
- Removed the support of OWL-QN method in MoreThuente algorithm because
it accidentally fails in early stages of iterations for some objectives.
Because of this change, <b>the OW-LQN method must be used with the
backtracking algorithm (::LBFGS_LINESEARCH_BACKTRACKING)</b>, or the
library returns ::LBFGSERR_INVALID_LINESEARCH.
- Renamed line search algorithms as follows:
- ::LBFGS_LINESEARCH_BACKTRACKING: regular Wolfe condition.
- ::LBFGS_LINESEARCH_BACKTRACKING_LOOSE: regular Wolfe condition.
- ::LBFGS_LINESEARCH_BACKTRACKING_STRONG: strong Wolfe condition.
- Source code clean-up.
- Version 1.6 (2008-11-02):
- Improved line-search algorithm with strong Wolfe condition, which was
contributed by Takashi Imamichi. This routine is now default for
::LBFGS_LINESEARCH_BACKTRACKING. The previous line search algorithm
with regular Wolfe condition is still available as
::LBFGS_LINESEARCH_BACKTRACKING_LOOSE.
- Configurable stop index for L1-norm computation. A member variable
::lbfgs_parameter_t::orthantwise_end was added to specify the index
number at which the library stops computing the L1 norm of the
variables. This is useful to prevent some variables from being
regularized by the OW-LQN method.
- A sample program written in C++ (sample/sample.cpp).
- Version 1.5 (2008-07-10):
- Configurable starting index for L1-norm computation. A member variable
::lbfgs_parameter_t::orthantwise_start was added to specify the index
number from which the library computes the L1 norm of the variables.
This is useful to prevent some variables from being regularized by the
OWL-QN method.
- Fixed a zero-division error when the initial variables have already
been a minimizer (reported by Takashi Imamichi). In this case, the
library returns ::LBFGS_ALREADY_MINIMIZED status code.
- Defined ::LBFGS_SUCCESS status code as zero; removed unused constants,
LBFGSFALSE and LBFGSTRUE.
- Fixed a compile error in an implicit down-cast.
- Version 1.4 (2008-04-25):
- Configurable line search algorithms. A member variable
::lbfgs_parameter_t::linesearch was added to choose either MoreThuente
method (::LBFGS_LINESEARCH_MORETHUENTE) or backtracking algorithm
(::LBFGS_LINESEARCH_BACKTRACKING).
- Fixed a bug: the previous version did not compute psuedo-gradients
properly in the line search routines for OWL-QN. This bug might quit
an iteration process too early when the OWL-QN routine was activated
(0 < ::lbfgs_parameter_t::orthantwise_c).
- Configure script for POSIX environments.
- SSE/SSE2 optimizations with GCC.
- New functions ::lbfgs_malloc and ::lbfgs_free to use SSE/SSE2 routines
transparently. It is uncessary to use these functions for libLBFGS built
without SSE/SSE2 routines; you can still use any memory allocators if
SSE/SSE2 routines are disabled in libLBFGS.
- Version 1.3 (2007-12-16):
- An API change. An argument was added to lbfgs() function to receive the
final value of the objective function. This argument can be set to
\c NULL if the final value is unnecessary.
- Fixed a null-pointer bug in the sample code (reported by Takashi Imamichi).
- Added build scripts for Microsoft Visual Studio 2005 and GCC.
- Added README file.
- Version 1.2 (2007-12-13):
- Fixed a serious bug in orthant-wise L-BFGS.
An important variable was used without initialization.
- Version 1.1 (2007-12-01):
- Implemented orthant-wise L-BFGS.
- Implemented lbfgs_parameter_init() function.
- Fixed several bugs.
- API documentation.
- Version 1.0 (2007-09-20):
- Initial release.
@section api Documentation
- @ref liblbfgs_api "libLBFGS API"
@section sample Sample code
@include sample.c
@section ack Acknowledgements
The L-BFGS algorithm is described in:
- Jorge Nocedal.
Updating Quasi-Newton Matrices with Limited Storage.
<i>Mathematics of Computation</i>, Vol. 35, No. 151, pp. 773--782, 1980.
- Dong C. Liu and Jorge Nocedal.
On the limited memory BFGS method for large scale optimization.
<i>Mathematical Programming</i> B, Vol. 45, No. 3, pp. 503-528, 1989.
The line search algorithms used in this implementation are described in:
- John E. Dennis and Robert B. Schnabel.
<i>Numerical Methods for Unconstrained Optimization and Nonlinear
Equations</i>, Englewood Cliffs, 1983.
- Jorge J. More and David J. Thuente.
Line search algorithm with guaranteed sufficient decrease.
<i>ACM Transactions on Mathematical Software (TOMS)</i>, Vol. 20, No. 3,
pp. 286-307, 1994.
This library also implements Orthant-Wise Limited-memory Quasi-Newton (OWL-QN)
method presented in:
- Galen Andrew and Jianfeng Gao.
Scalable training of L1-regularized log-linear models.
In <i>Proceedings of the 24th International Conference on Machine
Learning (ICML 2007)</i>, pp. 33-40, 2007.
Special thanks go to Yoshimasa Tsuruoka and Daisuke Okanohara for technical
information about OWL-QN.
Finally I would like to thank the original author, Jorge Nocedal, who has been
distributing the effieicnt and explanatory implementation in an open source
licence.
@section reference Reference
- <a href="http://www.ece.northwestern.edu/~nocedal/lbfgs.html">L-BFGS</a> by Jorge Nocedal.
- <a href="http://research.microsoft.com/en-us/downloads/b1eb1016-1738-4bd5-83a9-370c9d498a03/default.aspx">Orthant-Wise Limited-memory Quasi-Newton Optimizer for L1-regularized Objectives</a> by Galen Andrew.
- <a href="http://chasen.org/~taku/software/misc/lbfgs/">C port (via f2c)</a> by Taku Kudo.
- <a href="http://www.alglib.net/optimization/lbfgs.php">C#/C++/Delphi/VisualBasic6 port</a> in ALGLIB.
- <a href="http://cctbx.sourceforge.net/">Computational Crystallography Toolbox</a> includes
<a href="http://cctbx.sourceforge.net/current_cvs/c_plus_plus/namespacescitbx_1_1lbfgs.html">scitbx::lbfgs</a>.
*/
#endif/*__LBFGS_H__*/

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/*
* ANSI C implementation of vector operations.
*
* Copyright (c) 2007,2008,2009 Naoaki Okazaki
* All rights reserved.
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
/* $Id: arithmetic_ansi.h 50 2009-02-16 15:14:23Z naoaki $ */
#include <stdlib.h>
#include <memory.h>
#if LBFGS_FLOAT == 32 && LBFGS_IEEE_FLOAT
#define fsigndiff(x, y) (((*(uint32_t*)(x)) ^ (*(uint32_t*)(y))) & 0x80000000U)
#else
#define fsigndiff(x, y) (*(x) * (*(y) / fabs(*(y))) < 0.)
#endif/*LBFGS_IEEE_FLOAT*/
inline static void* vecalloc(size_t size)
{
void *memblock = malloc(size);
if (memblock) {
memset(memblock, 0, size);
}
return memblock;
}
inline static void vecfree(void *memblock)
{
free(memblock);
}
inline static void vecset(lbfgsfloatval_t *x, const lbfgsfloatval_t c, const int n)
{
int i;
for (i = 0;i < n;++i) {
x[i] = c;
}
}
inline static void veccpy(lbfgsfloatval_t *y, const lbfgsfloatval_t *x, const int n)
{
int i;
for (i = 0;i < n;++i) {
y[i] = x[i];
}
}
inline static void vecncpy(lbfgsfloatval_t *y, const lbfgsfloatval_t *x, const int n)
{
int i;
for (i = 0;i < n;++i) {
y[i] = -x[i];
}
}
inline static void vecadd(lbfgsfloatval_t *y, const lbfgsfloatval_t *x, const lbfgsfloatval_t c, const int n)
{
int i;
for (i = 0;i < n;++i) {
y[i] += c * x[i];
}
}
inline static void vecdiff(lbfgsfloatval_t *z, const lbfgsfloatval_t *x, const lbfgsfloatval_t *y, const int n)
{
int i;
for (i = 0;i < n;++i) {
z[i] = x[i] - y[i];
}
}
inline static void vecscale(lbfgsfloatval_t *y, const lbfgsfloatval_t c, const int n)
{
int i;
for (i = 0;i < n;++i) {
y[i] *= c;
}
}
inline static void vecmul(lbfgsfloatval_t *y, const lbfgsfloatval_t *x, const int n)
{
int i;
for (i = 0;i < n;++i) {
y[i] *= x[i];
}
}
inline static void vecdot(lbfgsfloatval_t* s, const lbfgsfloatval_t *x, const lbfgsfloatval_t *y, const int n)
{
int i;
*s = 0.;
for (i = 0;i < n;++i) {
*s += x[i] * y[i];
}
}
inline static void vec2norm(lbfgsfloatval_t* s, const lbfgsfloatval_t *x, const int n)
{
vecdot(s, x, x, n);
*s = (lbfgsfloatval_t)sqrt(*s);
}
inline static void vec2norminv(lbfgsfloatval_t* s, const lbfgsfloatval_t *x, const int n)
{
vec2norm(s, x, n);
*s = (lbfgsfloatval_t)(1.0 / *s);
}

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/*
* SSE2 implementation of vector oprations (64bit double).
*
* Copyright (c) 2007,2008,2009 Naoaki Okazaki
* All rights reserved.
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
/* $Id: arithmetic_sse_double.h 50 2009-02-16 15:14:23Z naoaki $ */
#include <stdlib.h>
#include <malloc.h>
#include <memory.h>
#if 1400 <= _MSC_VER
#include <intrin.h>
#endif/*1400 <= _MSC_VER*/
#if HAVE_EMMINTRIN_H
#include <emmintrin.h>
#endif/*HAVE_EMMINTRIN_H*/
inline static void* vecalloc(size_t size)
{
#ifdef _MSC_VER
void *memblock = _aligned_malloc(size, 16);
#else
void *memblock = memalign(16, size);
#endif
if (memblock != NULL) {
memset(memblock, 0, size);
}
return memblock;
}
inline static void vecfree(void *memblock)
{
#ifdef _MSC_VER
_aligned_free(memblock);
#else
free(memblock);
#endif
}
#define fsigndiff(x, y) \
((_mm_movemask_pd(_mm_set_pd(*(x), *(y))) + 1) & 0x002)
#define vecset(x, c, n) \
{ \
int i; \
__m128d XMM0 = _mm_set1_pd(c); \
for (i = 0;i < (n);i += 8) { \
_mm_store_pd((x)+i , XMM0); \
_mm_store_pd((x)+i+2, XMM0); \
_mm_store_pd((x)+i+4, XMM0); \
_mm_store_pd((x)+i+6, XMM0); \
} \
}
#define veccpy(y, x, n) \
{ \
int i; \
for (i = 0;i < (n);i += 8) { \
__m128d XMM0 = _mm_load_pd((x)+i ); \
__m128d XMM1 = _mm_load_pd((x)+i+2); \
__m128d XMM2 = _mm_load_pd((x)+i+4); \
__m128d XMM3 = _mm_load_pd((x)+i+6); \
_mm_store_pd((y)+i , XMM0); \
_mm_store_pd((y)+i+2, XMM1); \
_mm_store_pd((y)+i+4, XMM2); \
_mm_store_pd((y)+i+6, XMM3); \
} \
}
#define vecncpy(y, x, n) \
{ \
int i; \
for (i = 0;i < (n);i += 8) { \
__m128d XMM0 = _mm_setzero_pd(); \
__m128d XMM1 = _mm_setzero_pd(); \
__m128d XMM2 = _mm_setzero_pd(); \
__m128d XMM3 = _mm_setzero_pd(); \
__m128d XMM4 = _mm_load_pd((x)+i ); \
__m128d XMM5 = _mm_load_pd((x)+i+2); \
__m128d XMM6 = _mm_load_pd((x)+i+4); \
__m128d XMM7 = _mm_load_pd((x)+i+6); \
XMM0 = _mm_sub_pd(XMM0, XMM4); \
XMM1 = _mm_sub_pd(XMM1, XMM5); \
XMM2 = _mm_sub_pd(XMM2, XMM6); \
XMM3 = _mm_sub_pd(XMM3, XMM7); \
_mm_store_pd((y)+i , XMM0); \
_mm_store_pd((y)+i+2, XMM1); \
_mm_store_pd((y)+i+4, XMM2); \
_mm_store_pd((y)+i+6, XMM3); \
} \
}
#define vecadd(y, x, c, n) \
{ \
int i; \
__m128d XMM7 = _mm_set1_pd(c); \
for (i = 0;i < (n);i += 4) { \
__m128d XMM0 = _mm_load_pd((x)+i ); \
__m128d XMM1 = _mm_load_pd((x)+i+2); \
__m128d XMM2 = _mm_load_pd((y)+i ); \
__m128d XMM3 = _mm_load_pd((y)+i+2); \
XMM0 = _mm_mul_pd(XMM0, XMM7); \
XMM1 = _mm_mul_pd(XMM1, XMM7); \
XMM2 = _mm_add_pd(XMM2, XMM0); \
XMM3 = _mm_add_pd(XMM3, XMM1); \
_mm_store_pd((y)+i , XMM2); \
_mm_store_pd((y)+i+2, XMM3); \
} \
}
#define vecdiff(z, x, y, n) \
{ \
int i; \
for (i = 0;i < (n);i += 8) { \
__m128d XMM0 = _mm_load_pd((x)+i ); \
__m128d XMM1 = _mm_load_pd((x)+i+2); \
__m128d XMM2 = _mm_load_pd((x)+i+4); \
__m128d XMM3 = _mm_load_pd((x)+i+6); \
__m128d XMM4 = _mm_load_pd((y)+i ); \
__m128d XMM5 = _mm_load_pd((y)+i+2); \
__m128d XMM6 = _mm_load_pd((y)+i+4); \
__m128d XMM7 = _mm_load_pd((y)+i+6); \
XMM0 = _mm_sub_pd(XMM0, XMM4); \
XMM1 = _mm_sub_pd(XMM1, XMM5); \
XMM2 = _mm_sub_pd(XMM2, XMM6); \
XMM3 = _mm_sub_pd(XMM3, XMM7); \
_mm_store_pd((z)+i , XMM0); \
_mm_store_pd((z)+i+2, XMM1); \
_mm_store_pd((z)+i+4, XMM2); \
_mm_store_pd((z)+i+6, XMM3); \
} \
}
#define vecscale(y, c, n) \
{ \
int i; \
__m128d XMM7 = _mm_set1_pd(c); \
for (i = 0;i < (n);i += 4) { \
__m128d XMM0 = _mm_load_pd((y)+i ); \
__m128d XMM1 = _mm_load_pd((y)+i+2); \
XMM0 = _mm_mul_pd(XMM0, XMM7); \
XMM1 = _mm_mul_pd(XMM1, XMM7); \
_mm_store_pd((y)+i , XMM0); \
_mm_store_pd((y)+i+2, XMM1); \
} \
}
#define vecmul(y, x, n) \
{ \
int i; \
for (i = 0;i < (n);i += 8) { \
__m128d XMM0 = _mm_load_pd((x)+i ); \
__m128d XMM1 = _mm_load_pd((x)+i+2); \
__m128d XMM2 = _mm_load_pd((x)+i+4); \
__m128d XMM3 = _mm_load_pd((x)+i+6); \
__m128d XMM4 = _mm_load_pd((y)+i ); \
__m128d XMM5 = _mm_load_pd((y)+i+2); \
__m128d XMM6 = _mm_load_pd((y)+i+4); \
__m128d XMM7 = _mm_load_pd((y)+i+6); \
XMM4 = _mm_mul_pd(XMM4, XMM0); \
XMM5 = _mm_mul_pd(XMM5, XMM1); \
XMM6 = _mm_mul_pd(XMM6, XMM2); \
XMM7 = _mm_mul_pd(XMM7, XMM3); \
_mm_store_pd((y)+i , XMM4); \
_mm_store_pd((y)+i+2, XMM5); \
_mm_store_pd((y)+i+4, XMM6); \
_mm_store_pd((y)+i+6, XMM7); \
} \
}
#if 3 <= __SSE__
/*
Horizontal add with haddps SSE3 instruction. The work register (rw)
is unused.
*/
#define __horizontal_sum(r, rw) \
r = _mm_hadd_ps(r, r); \
r = _mm_hadd_ps(r, r);
#else
/*
Horizontal add with SSE instruction. The work register (rw) is used.
*/
#define __horizontal_sum(r, rw) \
rw = r; \
r = _mm_shuffle_ps(r, rw, _MM_SHUFFLE(1, 0, 3, 2)); \
r = _mm_add_ps(r, rw); \
rw = r; \
r = _mm_shuffle_ps(r, rw, _MM_SHUFFLE(2, 3, 0, 1)); \
r = _mm_add_ps(r, rw);
#endif
#define vecdot(s, x, y, n) \
{ \
int i; \
__m128d XMM0 = _mm_setzero_pd(); \
__m128d XMM1 = _mm_setzero_pd(); \
__m128d XMM2, XMM3, XMM4, XMM5; \
for (i = 0;i < (n);i += 4) { \
XMM2 = _mm_load_pd((x)+i ); \
XMM3 = _mm_load_pd((x)+i+2); \
XMM4 = _mm_load_pd((y)+i ); \
XMM5 = _mm_load_pd((y)+i+2); \
XMM2 = _mm_mul_pd(XMM2, XMM4); \
XMM3 = _mm_mul_pd(XMM3, XMM5); \
XMM0 = _mm_add_pd(XMM0, XMM2); \
XMM1 = _mm_add_pd(XMM1, XMM3); \
} \
XMM0 = _mm_add_pd(XMM0, XMM1); \
XMM1 = _mm_shuffle_pd(XMM0, XMM0, _MM_SHUFFLE2(1, 1)); \
XMM0 = _mm_add_pd(XMM0, XMM1); \
_mm_store_sd((s), XMM0); \
}
#define vec2norm(s, x, n) \
{ \
int i; \
__m128d XMM0 = _mm_setzero_pd(); \
__m128d XMM1 = _mm_setzero_pd(); \
__m128d XMM2, XMM3, XMM4, XMM5; \
for (i = 0;i < (n);i += 4) { \
XMM2 = _mm_load_pd((x)+i ); \
XMM3 = _mm_load_pd((x)+i+2); \
XMM4 = XMM2; \
XMM5 = XMM3; \
XMM2 = _mm_mul_pd(XMM2, XMM4); \
XMM3 = _mm_mul_pd(XMM3, XMM5); \
XMM0 = _mm_add_pd(XMM0, XMM2); \
XMM1 = _mm_add_pd(XMM1, XMM3); \
} \
XMM0 = _mm_add_pd(XMM0, XMM1); \
XMM1 = _mm_shuffle_pd(XMM0, XMM0, _MM_SHUFFLE2(1, 1)); \
XMM0 = _mm_add_pd(XMM0, XMM1); \
XMM0 = _mm_sqrt_pd(XMM0); \
_mm_store_sd((s), XMM0); \
}
#define vec2norminv(s, x, n) \
{ \
int i; \
__m128d XMM0 = _mm_setzero_pd(); \
__m128d XMM1 = _mm_setzero_pd(); \
__m128d XMM2, XMM3, XMM4, XMM5; \
for (i = 0;i < (n);i += 4) { \
XMM2 = _mm_load_pd((x)+i ); \
XMM3 = _mm_load_pd((x)+i+2); \
XMM4 = XMM2; \
XMM5 = XMM3; \
XMM2 = _mm_mul_pd(XMM2, XMM4); \
XMM3 = _mm_mul_pd(XMM3, XMM5); \
XMM0 = _mm_add_pd(XMM0, XMM2); \
XMM1 = _mm_add_pd(XMM1, XMM3); \
} \
XMM2 = _mm_set1_pd(1.0); \
XMM0 = _mm_add_pd(XMM0, XMM1); \
XMM1 = _mm_shuffle_pd(XMM0, XMM0, _MM_SHUFFLE2(1, 1)); \
XMM0 = _mm_add_pd(XMM0, XMM1); \
XMM0 = _mm_sqrt_pd(XMM0); \
XMM2 = _mm_div_pd(XMM2, XMM0); \
_mm_store_sd((s), XMM2); \
}

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/*
* SSE/SSE3 implementation of vector oprations (32bit float).
*
* Copyright (c) 2007,2008,2009 Naoaki Okazaki
* All rights reserved.
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
/* $Id: arithmetic_sse_float.h 50 2009-02-16 15:14:23Z naoaki $ */
#include <stdlib.h>
#include <malloc.h>
#include <memory.h>
#if 1400 <= _MSC_VER
#include <intrin.h>
#endif/*_MSC_VER*/
#if HAVE_XMMINTRIN_H
#include <xmmintrin.h>
#endif/*HAVE_XMMINTRIN_H*/
#if LBFGS_FLOAT == 32 && LBFGS_IEEE_FLOAT
#define fsigndiff(x, y) (((*(uint32_t*)(x)) ^ (*(uint32_t*)(y))) & 0x80000000U)
#else
#define fsigndiff(x, y) (*(x) * (*(y) / fabs(*(y))) < 0.)
#endif/*LBFGS_IEEE_FLOAT*/
inline static void* vecalloc(size_t size)
{
void *memblock = _aligned_malloc(size, 16);
if (memblock != NULL) {
memset(memblock, 0, size);
}
return memblock;
}
inline static void vecfree(void *memblock)
{
_aligned_free(memblock);
}
#define vecset(x, c, n) \
{ \
int i; \
__m128 XMM0 = _mm_set_ps1(c); \
for (i = 0;i < (n);i += 16) { \
_mm_store_ps((x)+i , XMM0); \
_mm_store_ps((x)+i+ 4, XMM0); \
_mm_store_ps((x)+i+ 8, XMM0); \
_mm_store_ps((x)+i+12, XMM0); \
} \
}
#define veccpy(y, x, n) \
{ \
int i; \
for (i = 0;i < (n);i += 16) { \
__m128 XMM0 = _mm_load_ps((x)+i ); \
__m128 XMM1 = _mm_load_ps((x)+i+ 4); \
__m128 XMM2 = _mm_load_ps((x)+i+ 8); \
__m128 XMM3 = _mm_load_ps((x)+i+12); \
_mm_store_ps((y)+i , XMM0); \
_mm_store_ps((y)+i+ 4, XMM1); \
_mm_store_ps((y)+i+ 8, XMM2); \
_mm_store_ps((y)+i+12, XMM3); \
} \
}
#define vecncpy(y, x, n) \
{ \
int i; \
const uint32_t mask = 0x80000000; \
__m128 XMM4 = _mm_load_ps1((float*)&mask); \
for (i = 0;i < (n);i += 16) { \
__m128 XMM0 = _mm_load_ps((x)+i ); \
__m128 XMM1 = _mm_load_ps((x)+i+ 4); \
__m128 XMM2 = _mm_load_ps((x)+i+ 8); \
__m128 XMM3 = _mm_load_ps((x)+i+12); \
XMM0 = _mm_xor_ps(XMM0, XMM4); \
XMM1 = _mm_xor_ps(XMM1, XMM4); \
XMM2 = _mm_xor_ps(XMM2, XMM4); \
XMM3 = _mm_xor_ps(XMM3, XMM4); \
_mm_store_ps((y)+i , XMM0); \
_mm_store_ps((y)+i+ 4, XMM1); \
_mm_store_ps((y)+i+ 8, XMM2); \
_mm_store_ps((y)+i+12, XMM3); \
} \
}
#define vecadd(y, x, c, n) \
{ \
int i; \
__m128 XMM7 = _mm_set_ps1(c); \
for (i = 0;i < (n);i += 8) { \
__m128 XMM0 = _mm_load_ps((x)+i ); \
__m128 XMM1 = _mm_load_ps((x)+i+4); \
__m128 XMM2 = _mm_load_ps((y)+i ); \
__m128 XMM3 = _mm_load_ps((y)+i+4); \
XMM0 = _mm_mul_ps(XMM0, XMM7); \
XMM1 = _mm_mul_ps(XMM1, XMM7); \
XMM2 = _mm_add_ps(XMM2, XMM0); \
XMM3 = _mm_add_ps(XMM3, XMM1); \
_mm_store_ps((y)+i , XMM2); \
_mm_store_ps((y)+i+4, XMM3); \
} \
}
#define vecdiff(z, x, y, n) \
{ \
int i; \
for (i = 0;i < (n);i += 16) { \
__m128 XMM0 = _mm_load_ps((x)+i ); \
__m128 XMM1 = _mm_load_ps((x)+i+ 4); \
__m128 XMM2 = _mm_load_ps((x)+i+ 8); \
__m128 XMM3 = _mm_load_ps((x)+i+12); \
__m128 XMM4 = _mm_load_ps((y)+i ); \
__m128 XMM5 = _mm_load_ps((y)+i+ 4); \
__m128 XMM6 = _mm_load_ps((y)+i+ 8); \
__m128 XMM7 = _mm_load_ps((y)+i+12); \
XMM0 = _mm_sub_ps(XMM0, XMM4); \
XMM1 = _mm_sub_ps(XMM1, XMM5); \
XMM2 = _mm_sub_ps(XMM2, XMM6); \
XMM3 = _mm_sub_ps(XMM3, XMM7); \
_mm_store_ps((z)+i , XMM0); \
_mm_store_ps((z)+i+ 4, XMM1); \
_mm_store_ps((z)+i+ 8, XMM2); \
_mm_store_ps((z)+i+12, XMM3); \
} \
}
#define vecscale(y, c, n) \
{ \
int i; \
__m128 XMM7 = _mm_set_ps1(c); \
for (i = 0;i < (n);i += 8) { \
__m128 XMM0 = _mm_load_ps((y)+i ); \
__m128 XMM1 = _mm_load_ps((y)+i+4); \
XMM0 = _mm_mul_ps(XMM0, XMM7); \
XMM1 = _mm_mul_ps(XMM1, XMM7); \
_mm_store_ps((y)+i , XMM0); \
_mm_store_ps((y)+i+4, XMM1); \
} \
}
#define vecmul(y, x, n) \
{ \
int i; \
for (i = 0;i < (n);i += 16) { \
__m128 XMM0 = _mm_load_ps((x)+i ); \
__m128 XMM1 = _mm_load_ps((x)+i+ 4); \
__m128 XMM2 = _mm_load_ps((x)+i+ 8); \
__m128 XMM3 = _mm_load_ps((x)+i+12); \
__m128 XMM4 = _mm_load_ps((y)+i ); \
__m128 XMM5 = _mm_load_ps((y)+i+ 4); \
__m128 XMM6 = _mm_load_ps((y)+i+ 8); \
__m128 XMM7 = _mm_load_ps((y)+i+12); \
XMM4 = _mm_mul_ps(XMM4, XMM0); \
XMM5 = _mm_mul_ps(XMM5, XMM1); \
XMM6 = _mm_mul_ps(XMM6, XMM2); \
XMM7 = _mm_mul_ps(XMM7, XMM3); \
_mm_store_ps((y)+i , XMM4); \
_mm_store_ps((y)+i+ 4, XMM5); \
_mm_store_ps((y)+i+ 8, XMM6); \
_mm_store_ps((y)+i+12, XMM7); \
} \
}
#if 3 <= __SSE__
/*
Horizontal add with haddps SSE3 instruction. The work register (rw)
is unused.
*/
#define __horizontal_sum(r, rw) \
r = _mm_hadd_ps(r, r); \
r = _mm_hadd_ps(r, r);
#else
/*
Horizontal add with SSE instruction. The work register (rw) is used.
*/
#define __horizontal_sum(r, rw) \
rw = r; \
r = _mm_shuffle_ps(r, rw, _MM_SHUFFLE(1, 0, 3, 2)); \
r = _mm_add_ps(r, rw); \
rw = r; \
r = _mm_shuffle_ps(r, rw, _MM_SHUFFLE(2, 3, 0, 1)); \
r = _mm_add_ps(r, rw);
#endif
#define vecdot(s, x, y, n) \
{ \
int i; \
__m128 XMM0 = _mm_setzero_ps(); \
__m128 XMM1 = _mm_setzero_ps(); \
__m128 XMM2, XMM3, XMM4, XMM5; \
for (i = 0;i < (n);i += 8) { \
XMM2 = _mm_load_ps((x)+i ); \
XMM3 = _mm_load_ps((x)+i+4); \
XMM4 = _mm_load_ps((y)+i ); \
XMM5 = _mm_load_ps((y)+i+4); \
XMM2 = _mm_mul_ps(XMM2, XMM4); \
XMM3 = _mm_mul_ps(XMM3, XMM5); \
XMM0 = _mm_add_ps(XMM0, XMM2); \
XMM1 = _mm_add_ps(XMM1, XMM3); \
} \
XMM0 = _mm_add_ps(XMM0, XMM1); \
__horizontal_sum(XMM0, XMM1); \
_mm_store_ss((s), XMM0); \
}
#define vec2norm(s, x, n) \
{ \
int i; \
__m128 XMM0 = _mm_setzero_ps(); \
__m128 XMM1 = _mm_setzero_ps(); \
__m128 XMM2, XMM3; \
for (i = 0;i < (n);i += 8) { \
XMM2 = _mm_load_ps((x)+i ); \
XMM3 = _mm_load_ps((x)+i+4); \
XMM2 = _mm_mul_ps(XMM2, XMM2); \
XMM3 = _mm_mul_ps(XMM3, XMM3); \
XMM0 = _mm_add_ps(XMM0, XMM2); \
XMM1 = _mm_add_ps(XMM1, XMM3); \
} \
XMM0 = _mm_add_ps(XMM0, XMM1); \
__horizontal_sum(XMM0, XMM1); \
XMM2 = XMM0; \
XMM1 = _mm_rsqrt_ss(XMM0); \
XMM3 = XMM1; \
XMM1 = _mm_mul_ss(XMM1, XMM1); \
XMM1 = _mm_mul_ss(XMM1, XMM3); \
XMM1 = _mm_mul_ss(XMM1, XMM0); \
XMM1 = _mm_mul_ss(XMM1, _mm_set_ss(-0.5f)); \
XMM3 = _mm_mul_ss(XMM3, _mm_set_ss(1.5f)); \
XMM3 = _mm_add_ss(XMM3, XMM1); \
XMM3 = _mm_mul_ss(XMM3, XMM2); \
_mm_store_ss((s), XMM3); \
}
#define vec2norminv(s, x, n) \
{ \
int i; \
__m128 XMM0 = _mm_setzero_ps(); \
__m128 XMM1 = _mm_setzero_ps(); \
__m128 XMM2, XMM3; \
for (i = 0;i < (n);i += 16) { \
XMM2 = _mm_load_ps((x)+i ); \
XMM3 = _mm_load_ps((x)+i+4); \
XMM2 = _mm_mul_ps(XMM2, XMM2); \
XMM3 = _mm_mul_ps(XMM3, XMM3); \
XMM0 = _mm_add_ps(XMM0, XMM2); \
XMM1 = _mm_add_ps(XMM1, XMM3); \
} \
XMM0 = _mm_add_ps(XMM0, XMM1); \
__horizontal_sum(XMM0, XMM1); \
XMM2 = XMM0; \
XMM1 = _mm_rsqrt_ss(XMM0); \
XMM3 = XMM1; \
XMM1 = _mm_mul_ss(XMM1, XMM1); \
XMM1 = _mm_mul_ss(XMM1, XMM3); \
XMM1 = _mm_mul_ss(XMM1, XMM0); \
XMM1 = _mm_mul_ss(XMM1, _mm_set_ss(-0.5f)); \
XMM3 = _mm_mul_ss(XMM3, _mm_set_ss(1.5f)); \
XMM3 = _mm_add_ss(XMM3, XMM1); \
_mm_store_ss((s), XMM3); \
}

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#include <string.h>
#include "YapInterface.h"
#include <lbfgs.h>
#include <stdio.h>
/*
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/>.
*/
// These constants describe the internal state
#define OPTIMIZER_STATUS_NONE 0
#define OPTIMIZER_STATUS_INITIALIZED 1
#define OPTIMIZER_STATUS_RUNNING 2
#define OPTIMIZER_STATUS_CB_EVAL 3
#define OPTIMIZER_STATUS_CB_PROGRESS 4
int optimizer_status=OPTIMIZER_STATUS_NONE; // the internal state
int n; // the size of the parameter vector
lbfgsfloatval_t *x; // pointer to the parameter vector x[0],...,x[n-1]
lbfgsfloatval_t *g; // pointer to the gradient vector g[0],...,g[n-1]
lbfgs_parameter_t param; // the parameters used for lbfgs
char buffer [2048]; // this buffer is used for creating the atoms to call
static lbfgsfloatval_t evaluate(
void *instance,
const lbfgsfloatval_t *x,
lbfgsfloatval_t *g_tmp,
const int n,
const lbfgsfloatval_t step
)
{
YAP_Term error;
YAP_Term call;
YAP_Bool result;
if (sprintf(buffer,"'$lbfgs_callback_evaluate'(FX,%i,%f)",n,step)<0) {
printf("ERROR: Creating call atom at evaluate function.\n");
return 0;
}
g=g_tmp;
call=YAP_ReadBuffer(buffer,&error);
optimizer_status=OPTIMIZER_STATUS_CB_EVAL;
result=YAP_CallProlog(call);
optimizer_status=OPTIMIZER_STATUS_RUNNING;
if (result==FALSE) {
printf("ERROR: Calling the evaluate call back function in YAP.\n");
// Goal did not succeed
return FALSE;
}
if (YAP_IsFloatTerm(YAP_ArgOfTerm(1,call))) {
return (lbfgsfloatval_t) YAP_FloatOfTerm(YAP_ArgOfTerm(1,call));
} else if (YAP_IsIntTerm(YAP_ArgOfTerm(1,call))) {
return (lbfgsfloatval_t) YAP_IntOfTerm(YAP_ArgOfTerm(1,call));
}
printf("ERROR: The evaluate call back function did not return a number as first argument.\n");
return 0;
}
static int progress(
void *instance,
const lbfgsfloatval_t *local_x,
const lbfgsfloatval_t *local_g,
const lbfgsfloatval_t fx,
const lbfgsfloatval_t xnorm,
const lbfgsfloatval_t gnorm,
const lbfgsfloatval_t step,
int n,
int k,
int ls
)
{
YAP_Term error;
YAP_Term call;
YAP_Bool result;
if (sprintf(buffer,"'$lbfgs_callback_progress'(%f,%f,%f,%f,%i,%i,%i,STOP)",fx,xnorm,gnorm,step,n,k,ls)<0) {
printf("ERROR: Creating atom at progress call back.\n");
return 1;
}
call=YAP_ReadBuffer(buffer,&error);
optimizer_status=OPTIMIZER_STATUS_CB_PROGRESS;
result=YAP_CallProlog(call);
optimizer_status=OPTIMIZER_STATUS_RUNNING;
if (result==FALSE) {
printf("ERROR: Calling the progress call back function in YAP.\n");
// Goal did not succeed
return FALSE;
}
if (YAP_IsIntTerm(YAP_ArgOfTerm(8,call))) {
return YAP_IntOfTerm(YAP_ArgOfTerm(8,call));
}
printf("ERROR: The progress call back function did not return an integer as last argument\n");
return 1;
}
static int set_x_value(void) {
YAP_Term t1=YAP_ARG1;
YAP_Term t2=YAP_ARG2;
int i=0;
if (optimizer_status!=OPTIMIZER_STATUS_INITIALIZED) {
printf("ERROR: set_x_value/2 can be called only when the optimizer is initialized and not running.\n");
return FALSE;
}
if (YAP_IsIntTerm(t1)) {
i=YAP_IntOfTerm(t1);
} else {
return FALSE;
}
if (i<0 || i>=n) {
printf("ERROR: invalid index for set_x_value/2.\n");
return FALSE;
}
if (YAP_IsFloatTerm(t2)) {
x[i]=(lbfgsfloatval_t) YAP_FloatOfTerm(t2);
} else if (YAP_IsIntTerm(t2)) {
x[i]=(lbfgsfloatval_t) YAP_IntOfTerm(t2);
} else {
return FALSE;
}
return TRUE;
}
static int get_x_value(void) {
YAP_Term t1=YAP_ARG1;
YAP_Term t2=YAP_ARG2;
int i=0;
if (optimizer_status==OPTIMIZER_STATUS_NONE) {
printf("ERROR: set_x_value/2 can be called only when the optimizer is initialized.\n");
return FALSE;
}
if (YAP_IsIntTerm(t1)) {
i=YAP_IntOfTerm(t1);
} else {
return FALSE;
}
if (i<0 || i>=n) {
printf("ERROR: invalid index for set_x_value/2.\n");
return FALSE;
}
return YAP_Unify(t2,YAP_MkFloatTerm(x[i]));
}
static int set_g_value(void) {
YAP_Term t1=YAP_ARG1;
YAP_Term t2=YAP_ARG2;
int i=0;
if (optimizer_status != OPTIMIZER_STATUS_CB_EVAL) {
printf("ERROR: optimizer_set_g/2 can only be called by the evaluation call back function.\n");
return FALSE;
}
if (YAP_IsIntTerm(t1)) {
i=YAP_IntOfTerm(t1);
} else {
return FALSE;
}
if (i<0 || i>=n) {
return FALSE;
}
if (YAP_IsFloatTerm(t2)) {
g[i]=(lbfgsfloatval_t) YAP_FloatOfTerm(t2);
} else if (YAP_IsIntTerm(t2)) {
g[i]=(lbfgsfloatval_t) YAP_IntOfTerm(t2);
} else {
return FALSE;
}
return TRUE;
}
static int get_g_value(void) {
YAP_Term t1=YAP_ARG1;
YAP_Term t2=YAP_ARG2;
int i=0;
if (optimizer_status != OPTIMIZER_STATUS_RUNNING && optimizer_status != OPTIMIZER_STATUS_CB_EVAL && optimizer_status != OPTIMIZER_STATUS_CB_PROGRESS) {
printf("ERROR: optimizer_get_g/2 can only be called while the optimizer is running.\n");
return FALSE;
}
if (YAP_IsIntTerm(t1)) {
i=YAP_IntOfTerm(t1);
} else {
return FALSE;
}
if (i<0 || i>=n) {
return FALSE;
}
return YAP_Unify(t2,YAP_MkFloatTerm(g[i]));
}
static int optimizer_initialize(void) {
YAP_Term t1 = YAP_ARG1;
int temp_n=0;
if (optimizer_status!=OPTIMIZER_STATUS_NONE) {
printf("ERROR: Optimizer has already been initialized. Please call optimizer_finalize/0 first.\n");
return FALSE;
}
if (! YAP_IsIntTerm(t1)) {
return FALSE;
}
temp_n=YAP_IntOfTerm(t1);
if (temp_n<1) {
return FALSE;
}
x = lbfgs_malloc(temp_n);
if (x == NULL) {
printf("ERROR: Failed to allocate a memory block for variables.\n");
return FALSE;
}
n=temp_n;
optimizer_status=OPTIMIZER_STATUS_INITIALIZED;
return TRUE;
}
static int optimizer_run(void) {
YAP_Term t1 = YAP_ARG1;
YAP_Term t2 = YAP_ARG2;
int ret = 0;
lbfgsfloatval_t fx;
lbfgsfloatval_t * tmp_x=x;
if (optimizer_status == OPTIMIZER_STATUS_NONE) {
printf("ERROR: Memory for parameter vector not initialized, please call optimizer_initialize/1 first.\n");
return FALSE;
}
if (optimizer_status != OPTIMIZER_STATUS_INITIALIZED) {
printf("ERROR: Optimizer is running right now. Please wait till it is finished.\n");
return FALSE;
}
// both arguments have to be variables
if (! YAP_IsVarTerm(t1) || ! YAP_IsVarTerm(t2)) {
return FALSE;
}
optimizer_status = OPTIMIZER_STATUS_RUNNING;
ret = lbfgs(n, x, &fx, evaluate, progress, NULL, &param);
x=tmp_x;
optimizer_status = OPTIMIZER_STATUS_INITIALIZED;
YAP_Unify(t1,YAP_MkFloatTerm(fx));
YAP_Unify(t2,YAP_MkIntTerm(ret));
return TRUE;
}
static int optimizer_finalize( void ) {
if (optimizer_status == OPTIMIZER_STATUS_NONE) {
printf("Error: Optimizer is not initialized.\n");
return FALSE;
}
if (optimizer_status == OPTIMIZER_STATUS_INITIALIZED) {
lbfgs_free(x);
x=NULL;
n=0;
optimizer_status = OPTIMIZER_STATUS_NONE;
return TRUE;
}
printf("ERROR: Optimizer is running right now. Please wait till it is finished.\n");
return FALSE;
}
static int optimizer_set_parameter( void ) {
YAP_Term t1 = YAP_ARG1;
YAP_Term t2 = YAP_ARG2;
if (optimizer_status != OPTIMIZER_STATUS_NONE && optimizer_status != OPTIMIZER_STATUS_INITIALIZED){
printf("ERROR: Optimizer is running right now. Please wait till it is finished.\n");
return FALSE;
}
if (! YAP_IsAtomTerm(t1)) {
return FALSE;
}
const char* name=YAP_AtomName(YAP_AtomOfTerm(t1));
if ((strcmp(name, "m") == 0)) {
if (! YAP_IsIntTerm(t2)) {
return FALSE;
}
param.m = YAP_IntOfTerm(t2);
} else if ((strcmp(name, "epsilon") == 0)) {
lbfgsfloatval_t v;
if (YAP_IsFloatTerm(t2)) {
v=YAP_FloatOfTerm(t2);
} else if (YAP_IsIntTerm(t2)) {
v=(lbfgsfloatval_t) YAP_IntOfTerm(t2);
} else {
return FALSE;
}
param.epsilon=v;
} else if ((strcmp(name, "past") == 0)) {
if (! YAP_IsIntTerm(t2)) {
return FALSE;
}
param.past = YAP_IntOfTerm(t2);
} else if ((strcmp(name, "delta") == 0)) {
lbfgsfloatval_t v;
if (YAP_IsFloatTerm(t2)) {
v=YAP_FloatOfTerm(t2);
} else if (YAP_IsIntTerm(t2)) {
v=(lbfgsfloatval_t) YAP_IntOfTerm(t2);
} else {
return FALSE;
}
param.delta=v;
} else if ((strcmp(name, "max_iterations") == 0)) {
if (! YAP_IsIntTerm(t2)) {
return FALSE;
}
param.max_iterations = YAP_IntOfTerm(t2);
} else if ((strcmp(name, "linesearch") == 0)) {
if (! YAP_IsIntTerm(t2)) {
return FALSE;
}
param.linesearch = YAP_IntOfTerm(t2);
} else if ((strcmp(name, "max_linesearch") == 0)) {
if (! YAP_IsIntTerm(t2)) {
return FALSE;
}
param.max_linesearch = YAP_IntOfTerm(t2);
} else if ((strcmp(name, "min_step") == 0)) {
lbfgsfloatval_t v;
if (YAP_IsFloatTerm(t2)) {
v=YAP_FloatOfTerm(t2);
} else if (YAP_IsIntTerm(t2)) {
v=(lbfgsfloatval_t) YAP_IntOfTerm(t2);
} else {
return FALSE;
}
param.min_step=v;
} else if ((strcmp(name, "max_step") == 0)) {
lbfgsfloatval_t v;
if (YAP_IsFloatTerm(t2)) {
v=YAP_FloatOfTerm(t2);
} else if (YAP_IsIntTerm(t2)) {
v=(lbfgsfloatval_t) YAP_IntOfTerm(t2);
} else {
return FALSE;
}
param.max_step=v;
} else if ((strcmp(name, "ftol") == 0)) {
lbfgsfloatval_t v;
if (YAP_IsFloatTerm(t2)) {
v=YAP_FloatOfTerm(t2);
} else if (YAP_IsIntTerm(t2)) {
v=(lbfgsfloatval_t) YAP_IntOfTerm(t2);
} else {
return FALSE;
}
param.ftol=v;
} else if ((strcmp(name, "gtol") == 0)) {
lbfgsfloatval_t v;
if (YAP_IsFloatTerm(t2)) {
v=YAP_FloatOfTerm(t2);
} else if (YAP_IsIntTerm(t2)) {
v=(lbfgsfloatval_t) YAP_IntOfTerm(t2);
} else {
return FALSE;
}
param.gtol=v;
} else if ((strcmp(name, "xtol") == 0)) {
lbfgsfloatval_t v;
if (YAP_IsFloatTerm(t2)) {
v=YAP_FloatOfTerm(t2);
} else if (YAP_IsIntTerm(t2)) {
v=(lbfgsfloatval_t) YAP_IntOfTerm(t2);
} else {
return FALSE;
}
param.xtol=v;
} else if ((strcmp(name, "orthantwise_c") == 0)) {
lbfgsfloatval_t v;
if (YAP_IsFloatTerm(t2)) {
v=YAP_FloatOfTerm(t2);
} else if (YAP_IsIntTerm(t2)) {
v=(lbfgsfloatval_t) YAP_IntOfTerm(t2);
} else {
return FALSE;
}
param.orthantwise_c=v;
} else if ((strcmp(name, "orthantwise_start") == 0)) {
if (! YAP_IsIntTerm(t2)) {
return FALSE;
}
param.orthantwise_start = YAP_IntOfTerm(t2);
} else if ((strcmp(name, "orthantwise_end") == 0)) {
if (! YAP_IsIntTerm(t2)) {
return FALSE;
}
param.orthantwise_end = YAP_IntOfTerm(t2);
} else {
printf("ERROR: The parameter %s is unknown.\n",name);
return FALSE;
}
return TRUE;
}
static int optimizer_get_parameter( void ) {
YAP_Term t1 = YAP_ARG1;
YAP_Term t2 = YAP_ARG2;
if (! YAP_IsAtomTerm(t1)) {
return FALSE;
}
const char* name=YAP_AtomName(YAP_AtomOfTerm(t1));
if ((strcmp(name, "m") == 0)) {
return YAP_Unify(t2,YAP_MkIntTerm(param.m));
} else if ((strcmp(name, "epsilon") == 0)) {
return YAP_Unify(t2,YAP_MkFloatTerm(param.epsilon));
} else if ((strcmp(name, "past") == 0)) {
return YAP_Unify(t2,YAP_MkIntTerm(param.past));
} else if ((strcmp(name, "delta") == 0)) {
return YAP_Unify(t2,YAP_MkFloatTerm(param.delta));
} else if ((strcmp(name, "max_iterations") == 0)) {
return YAP_Unify(t2,YAP_MkIntTerm(param.max_iterations));
} else if ((strcmp(name, "linesearch") == 0)) {
return YAP_Unify(t2,YAP_MkIntTerm(param.linesearch));
} else if ((strcmp(name, "max_linesearch") == 0)) {
return YAP_Unify(t2,YAP_MkIntTerm(param.max_linesearch));
} else if ((strcmp(name, "min_step") == 0)) {
return YAP_Unify(t2,YAP_MkFloatTerm(param.min_step));
} else if ((strcmp(name, "max_step") == 0)) {
return YAP_Unify(t2,YAP_MkFloatTerm(param.max_step));
} else if ((strcmp(name, "ftol") == 0)) {
return YAP_Unify(t2,YAP_MkFloatTerm(param.ftol));
} else if ((strcmp(name, "gtol") == 0)) {
return YAP_Unify(t2,YAP_MkFloatTerm(param.gtol));
} else if ((strcmp(name, "xtol") == 0)) {
return YAP_Unify(t2,YAP_MkFloatTerm(param.xtol));
} else if ((strcmp(name, "orthantwise_c") == 0)) {
return YAP_Unify(t2,YAP_MkFloatTerm(param.orthantwise_c));
} else if ((strcmp(name, "orthantwise_start") == 0)) {
return YAP_Unify(t2,YAP_MkIntTerm(param.orthantwise_start));
} else if ((strcmp(name, "orthantwise_end") == 0)) {
return YAP_Unify(t2,YAP_MkIntTerm(param.orthantwise_end));
}
printf("ERROR: The parameter %s is unknown.\n",name);
return FALSE;
}
void init_lbfgs_predicates( void )
{
//Initialize the parameters for the L-BFGS optimization.
lbfgs_parameter_init(&param);
YAP_UserCPredicate("optimizer_reserve_memory",optimizer_initialize,1);
YAP_UserCPredicate("optimizer_run",optimizer_run,2);
YAP_UserCPredicate("optimizer_free_memory",optimizer_finalize,0);
YAP_UserCPredicate("optimizer_set_x",set_x_value,2);
YAP_UserCPredicate("optimizer_get_x",get_x_value,2);
YAP_UserCPredicate("optimizer_set_g",set_g_value,2);
YAP_UserCPredicate("optimizer_get_g",get_g_value,2);
YAP_UserCPredicate("optimizer_set_parameter",optimizer_set_parameter,2);
YAP_UserCPredicate("optimizer_get_parameter",optimizer_get_parameter,2);
}