Algorithm::LBFGS

Algorithm::LBFGS(3pm) User Contributed Perl DocumentationAlgorithm::LBFGS(3pm)



NAME
       Algorithm::LBFGS - Perl extension for L-BFGS

SYNOPSIS
         use Algorithm::LBFGS;

         # create an L-BFGS optimizer
         my $o = Algorithm::LBFGS->new;

         # f(x) = (x1 - 1)^2 + (x2 + 2)^2
         # grad f(x) = (2 * (x1 - 1), 2 * (x2 + 2));
         my $eval_cb = sub {
             my $x = shift;
             my $f = ($x->[0] - 1) * ($x->[0] - 1) + ($x->[1] + 2) * ($x->[1] + 2);
             my $g = [ 2 * ($x->[0] - 1), 2 * ($x->[1] + 2) ];
             return ($f, $g);
         };

         my $x0 = [0.0, 0.0]; # initial point
         my $x = $o->fmin($eval_cb, $x0); # $x is supposed to be [ 1, -2 ];

DESCRIPTION
       L-BFGS (Limited-memory Broyden-Fletcher-Goldfarb-Shanno) is a quasi-
       Newton method for unconstrained optimization. This method is especially
       efficient on problems involving a large number of variables.

       Generally, it solves a problem described as following:

         min f(x), x = (x1, x2, ..., xn)

       Jorge Nocedal wrote a Fortran 77 version of this algorithm.

       <http://www.ece.northwestern.edu/~nocedal/lbfgs.html>

       And, Naoaki Okazaki rewrote it in pure C (liblbfgs).

       <http://www.chokkan.org/software/liblbfgs/index.html>

       This module is a Perl port of Naoaki Okazaki's C version.

   new
       "new" creates a L-BFGS optimizer with given parameters.

         my $o1 = new Algorithm::LBFGS(m => 5);
         my $o2 = new Algorithm::LBFGS(m => 3, eps => 1e-6);
         my $o3 = new Algorithm::LBFGS;

       If no parameter is specified explicitly, their default values are used.

       The parameter can be changed after the creation of the optimizer by
       "set_param". Also, they can be queryed by "get_param".

       Please refer to the "List of Parameters" for details about parameters.

   get_param
       Query the value of a parameter.

          my $o = Algorithm::LBFGS->new;
          print $o->get_param('epsilon'); # 1e-5

   set_param
       Change the values of one or several parameters.

          my $o = Algorithm::LBFGS->new;
          $o->set_param(epsilon => 1e-6, m => 7);

   fmin
       The prototype of "fmin" is like

         x = fmin(evaluation_cb, x0, progress_cb, user_data)

       As the name says, it finds a vector x which minimize the function f(x).

       "evaluation_cb" is a ref to the evaluation callback subroutine, "x0" is
       the initial point of the optimization algorithm, "progress_cb"
       (optional) is a ref to the progress callback subroutine, and
       "user_data" (optional) is a piece of extra data that client program
       want to pass to both "evaluation_cb" and "progress_cb".

       Client program can use "get_status" to find if any problem occured
       during the optimization after their calling "fmin". When the status is
       "LBFGS_OK", the returning value "x" (array ref) contains the optimized
       variables, otherwise, there may be some problems occured and the value
       in the returning "x" is undefined.

       evaluation_cb

       The ref to the evaluation callback subroutine.

       The evaluation callback subroutine is supposed to calculate the
       function value and gradient vector at a specified point "x". It is
       called automatically by "fmin" when an evaluation is needed.

       The client program need to make sure their evaluation callback
       subroutine has a prototype like

         (f, g) = evaluation_cb(x, step, user_data)

       "x" (array ref) is the current values of variables, "step" is the
       current step of the line search routine, "user_data" is the extra user
       data specified when calling "fmin".

       The evaluation callback subroutine is supposed to return both the
       function value "f" and the gradient vector "g" (array ref) at current
       "x".

       x0

       The initial point of the optimization algorithm.  The final result may
       depend on your choice of "x0".

       NOTE: The content of "x0" will be modified after calling "fmin".  When
       the algorithm terminates successfully, the content of "x0" will be
       replaced by the optimized variables, otherwise, the content of "x0" is
       undefined.

       progress_cb

       The ref to the progress callback subroutine.

       The progress callback subroutine is called by "fmin" at the end of each
       iteration, with information of current iteration. It is very useful for
       a client program to monitor the optimization progress.

       The client program need to make sure their progress callback subroutine
       has a prototype like

         s = progress_cb(x, g, fx, xnorm, gnorm, step, k, ls, user_data)

       "x" (array ref) is the current values of variables. "g" (array ref) is
       the current gradient vector. "fx" is the current function value.
       "xnorm" and "gnorm" is the L2 norm of "x" and "g". "step" is the line-
       search step used for this iteration. "k" is the iteration count. "ls"
       is the number of evaluations in this iteration. "user_data" is the
       extra user data specified when calling "fmin".

       The progress callback subroutine is supposed to return an indicating
       value "s" for "fmin" to decide whether the optimization should continue
       or stop. "fmin" continues to the next iteration when "s=0", otherwise,
       it terminates with status code "LBFGSERR_CANCELED".

       The client program can also pass string values to "progress_cb", which
       means it want to use a predefined progress callback subroutine. There
       are two predefined progress callback subroutines, 'verbose' and
       'logging'.  'verbose' just prints out all information of each
       iteration, while 'logging' logs the same information in an array ref
       provided by "user_data".

         ...

         # print out the iterations
         fmin($eval_cb, $x0, 'verbose');

         # log iterations information in the array ref $log
         my $log = [];

         fmin($eval_cb, $x0, 'logging', $log);

         use Data::Dumper;
         print Dumper $log;

       user_data

       The extra user data. It will be sent to both "evaluation_cb" and
       "progress_cb".

   get_status
       Get the status of previous call of "fmin".

         ...
         $o->fmin(...);

         # check the status
         if ($o->get_status eq 'LBFGS_OK') {
            ...
         }

         # print the status out
         print $o->get_status;

       The status code is a string, which could be one of those in the "List
       of Status Codes".

   status_ok
       This is a shortcut of saying "get_status" eq "LBFGS_OK".

         ...

         if ($o->fmin(...), $o->status_ok) {
             ...
         }

   List of Parameters
       m

       The number of corrections to approximate the inverse hessian matrix.

       The L-BFGS algorithm stores the computation results of previous "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 6. Values less than 3 are not
       recommended. Large values will result in excessive computing time.

       epsilon

       Epsilon for convergence test.

       This parameter determines the accuracy with which the solution is to be
       found. A minimization terminates when

         ||grad f(x)|| < epsilon * max(1, ||x||)

       where ||.|| denotes the Euclidean (L2) norm. The default value is 1e-5.

       max_iterations

       The maximum number of iterations.

       The L-BFGS algorithm 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
       0.

       max_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 20.

       min_step

       The minimum step of the line search routine.

       The default value is 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).

       max_step

       The maximum step of the line search.

       The default value is 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).

       ftol

       A parameter to control the accuracy of the line search routine.

       The default value is 1e-4. This parameter should be greater than zero
       and smaller than 0.5.

       gtol

       A parameter to control the accuracy of the line search routine.

       The default value is 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 0.1. This parameter shuold be greater than the ftol parameter
       (1e-4) and smaller than 1.0.

       xtol

       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.

       orthantwise_c

       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 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 module modify
       function and gradient evaluations from a client program suitably; a
       client program thus have only to return the function value f(x) and
       gradients grad f(x) as usual. The default value is zero.

   List of Status Codes
       LBFGS_OK

       No error occured.

       LBFGSERR_UNKNOWNERROR

       Unknown error.

       LBFGSERR_LOGICERROR

       Logic error.

       LBFGSERR_OUTOFMEMORY

       Insufficient memory.

       LBFGSERR_CANCELED

       The minimization process has been canceled.

       LBFGSERR_INVALID_N

       Invalid number of variables specified.

       LBFGSERR_INVALID_N_SSE

       Invalid number of variables (for SSE) specified.

       LBFGSERR_INVALID_MINSTEP

       Invalid parameter "max_step" specified.

       LBFGSERR_INVALID_MAXSTEP

       Invalid parameter "max_step" specified.

       LBFGSERR_INVALID_FTOL

       Invalid parameter "ftol" specified.

       LBFGSERR_INVALID_GTOL

       Invalid parameter "gtol" specified.

       LBFGSERR_INVALID_XTOL

       Invalid parameter "xtol" specified.

       LBFGSERR_INVALID_MAXLINESEARCH

       Invalid parameter "max_linesearch" specified.

       LBFGSERR_INVALID_ORTHANTWISE

       Invalid parameter "orthantwise_c" specified.

       LBFGSERR_OUTOFINTERVAL

       The line-search step went out of the interval of uncertainty.

       LBFGSERR_INCORRECT_TMINMAX

       A logic error occurred; alternatively, the interval of uncertainty
       became too small.

       LBFGSERR_ROUNDING_ERROR

       A rounding error occurred; alternatively, no line-search step satisfies
       the sufficient decrease and curvature conditions.

       LBFGSERR_MINIMUMSTEP

       The line-search step became smaller than "min_step".

       LBFGSERR_MAXIMUMSTEP

       The line-search step became larger than "max_step".

       LBFGSERR_MAXIMUMLINESEARCH

       The line-search routine reaches the maximum number of evaluations.

       LBFGSERR_MAXIMUMITERATION

       The algorithm routine reaches the maximum number of iterations.

       LBFGSERR_WIDTHTOOSMALL

       Relative width of the interval of uncertainty is at most "xtol".

       LBFGSERR_INVALIDPARAMETERS

       A logic error (negative line-search step) occurred.

       LBFGSERR_INCREASEGRADIENT

       The current search direction increases the objective function value.

SEE ALSO
       PDL, PDL::Opt::NonLinear

AUTHOR
       Laye Suen, <laye@cpan.org>

COPYRIGHT AND LICENSE
       Copyright (C) 1990, Jorge Nocedal

       Copyright (C) 2007, Naoaki Okazaki

       Copyright (C) 2008, Laye Suen

       This library is distributed under the term of the MIT license.

       <http://opensource.org/licenses/mit-license.php>

REFERENCE
        J. Nocedal. Updating Quasi-Newton Matrices with Limited Storage (1980)
       , Mathematics of Computation 35, pp. 773-782.
        D.C. Liu and J. Nocedal. On the Limited Memory Method for Large Scale
       Optimization (1989), Mathematical Programming B, 45, 3, pp. 503-528.
        Jorge Nocedal's Fortran 77 implementation,
       <http://www.ece.northwestern.edu/~nocedal/lbfgs.html>
        Naoaki Okazaki's C implementation (liblbfgs),
       <http://www.chokkan.org/software/liblbfgs/index.html>



perl v5.28.0                      2018-11-01             Algorithm::LBFGS(3pm)