NAME

Math::NLopt - Math::NLopt - Perl interface to the NLopt optimization library

VERSION

version 0.04

SYNOPSIS

use Math::NLopt ':algorithms';

my $opt = Math::NLopt->new( NLOPT_LD_MMA, 2 );
$opt->set_lower_bounds( [ -HUGE_VAL(), 0 ] );
$opt->set_min_objective( sub ( $x, $grad, $data { ... }  );
$opt->set_xtol_rel( ... );
my $output_pars = $opt->optimize( \@input_pars );

DESCRIPTION

NLopt is a

library for nonlinear local and global optimization, for functions
with and without gradient information. It is designed as a simple,
unified interface and packaging of several free/open-source
nonlinear optimization libraries.

Math::NLopt is a Perl binding to NLopt. It uses the Alien::NLopt module to find or install a Perl local instance of the NLopt library.

This version interfaces to Perl using native Perl arrays. A version which uses PDL ndarrays will be forthcoming.

The main documentation for NLopt may be found at https://nlopt.readthedocs.io/; this documentation focuses on the Perl specific implementation, which is more Perlish than the C API (and is very similar to the Python one).

Perl Interface

The Perl interface closely tracks the object oriented interface of NLopt, but uses methods rather than subroutine calls, e.g. translate the C

result = nlopt_<method>( opt, ... );

into

$result = $opt->method( ... );

However, the Perl API in general returns results directly, whereas the C interface returns a success/failure code and transfers data to and from a routine via its parameters. The Perl API, apart from that for the objective and constraint methods, uses parameters solely as input data for the methods. For example, the C API for starting the optimization process is

nlopt_result nlopt_optimize(nlopt_opt opt, double *x, double *opt_f);

where x is used for both passing in the initial model parameters as well as retrieving their final values. The final value of the optimization function is stored in opt_f. A code specifying the success or failure of the process is returned.

The Perl interface (similar to the Python and C+ versions) is

\@final = $opt->optimize( \@initial_pars );
$opt_f = $opt->last_optimum_value;
$result_code = $opt->last_optimize_result;

The Perl API will throw exceptions on failures, similar to the behavior of the C++ and Python API's. That behavior will be tunable in future releases.

Constants

Math::NLopt defines constants for the optimization algorithms, result codes, and utilities.

The algorithm constants have the same names as the NLopt constants, and may be imported individually by name or en-masse with the ':algorithms' tag:

use Math::NLopt 'NLOPT_LD_MMA';
use Math::NLopt ':algorithms';

Importing result codes is similar:

use Math::NLopt 'NLOPT_FORCED_STOP';
use Math::NLopt ':results';

As are the utility subroutines:

use Math::NLopt 'algorithm_from_string';
use Math::NLopt ':utils';

Callbacks

While NLopt performs the optimization of the objective function, it requires callback subroutines which return the value of the objective function or non-linear constraints. Such callback subroutines have a required calling signature documented below. The user can provide their own data structure containing additional information which will be passed to the callbacks, or they can access that information from closures.

Objective Functions

Objective functions callbacks are registered via either

$opt->set_min_objective( \&func, ?$data );
$opt->set_max_objective( \&func, ?$data );

where $data is an optional structure passed to the callback which can be used for any purpose.

The objective function has the signature

$value = sub ( \@params, \@gradient, $data ) { ... }

It returns is the value of the optimization function for the passed set parameters, @params.

if \@gradient is not undef, it must be filled in by the by the objective function.

$data is the structure registered with the callback. It will be undef if none was provided.

Non-linear Constraints

Nonlinear constraint callbacks are registered via either of

$opt->add_equality_constraint( \&func, ?$data, ?$tol = 0 );
$opt->add_inequality_constraint( \&func, ?$data, ?$tol = 0 );

where $data is an optional structure passed to the callback which can be used for any purpose, and $tol is a tolerance. Pass undef for $data if a tolerance is required but $data is not.

The callbacks have the same signature as the objective callbacks.

Vector-valued Constraints

Vector-valued constraint callbacks are registered via either of

$opt->add_equality_mconstraint( \&func, $m, ?$data, ?\@tol );
$opt->add_inequality_mconstraint( \&func, $m, ?$data, ?\@tol );

where $m is the length of the vector, $data is an optional structure passed on to the callback function, and @tol is an optional array of length $m containing the tolerance for each component of the vector

Vector valued constraints callbacks have the signature

sub ( \@result, \@params, \@gradient, $data ) { ... }

The $m length vector of constraints should be stored in \@result. If \@gradient is not undef, it is a $n x $m length array which should be filled by the callback.

$data is the optional structure passed to the callback.

Preconditioned Objectives

These are registered via one of

$opt->set_precond_min_objective( \&func, \&precond, ?$data);
$opt->set_precond_max_objective( \&func, \&precond, ?$data);

\&func has the same signature as before (see "Objective Functions"), and $data is as before.

The \&precond fallback has this signature:

sub (\@x, \@v, \@vpre, $data) {...}

\@x, \@v, and \@vpre are arrays of length $n. \@x, \@v are input and \@vpre should be filled in by the routine.

METHODS

Most methods have the same calling signature as their C versions, but not all!

add_equality_constraint

$opt->add_equality_constraint( \&func, ?$data, ?$tol = 0 );

add_equality_mconstraint

$opt->add_equality_mconstraint( \&func, $m, ?$data, ?\@tol );

add_inequality_constraint

$opt->add_inequality_constraint( \&func, ?$data, ?$tol = 0 );

add_inequality_mconstraint

$opt->add_inequality_mconstraint( \&func, $m, ?$data, ?\@tol );

force_stop

$opt->force_stop;

get_algorithm

$algorithm_int_id = $opt->get_algorithm;

get_dimension

$n = $opt->get_dimension;

get_errmsg

$string  $opt->get_errmsg;

get_force_stop

$stop = $opt->get_force_stop;

get_ftol_abs

$tol = $opt->get_ftol_abs;

get_ftol_rel

$tol = $opt->get_ftol_rel;

get_initial_step

\@steps = $opt->get_initial_step( \@init_x );

get_lower_bounds

\@lb = $opt->get_lower_bounds;

get_maxeval

$max_eval = $opt->get_maxeval;

get_maxtime

$max_time = $opt->get_maxtime;

get_numevals

$num_evals = $opt->get_numevals;

get_param

$val = $opt->get_param( $name, $defaultval);

Return parameter value, or $defaultval if not set.

get_population

$pop = $opt->get_population;

get_stopval

$val = $opt->get_stopval;

get_upper_bounds

\@ub = $opt->get_upper_bounds;

get_vector_storage

$dim = $opt->get_vector_storage;

get_x_weights

\@weights = $opt->get_x_weights;

get_xtol_abs

\@tol = $opt->get_xtol_abs;

get_xtol_rel

$tol = $opt->get_xtol_rel;

has_param

$bool = $opt->has_param( $name );

True if the parameter with $name was set.

nth_param

$name = $opt->nth_param( $i );

Return the name of algorithm specific parameter $i.

last_optimize_result

$result_code = $opt->last_optimize_result;

Return the result code after an optimization.

last_optimum_value

$min_f = $opt->last_optimum_value;

Return the minimum objective value obtained after an optimization.

num_params

$n_algo_params = $opt->num_params;

Return the number of algorithm specific parameters.

optimize

\@optimized_pars = $opt->optimize( \@input_pars );

remove_equality_constraints

$opt->remove_equality_constraints;

remove_inequality_constraints

$opt->remove_inequality_constraints;

set_force_stop

$opt->set_force_stop( $val );

set_ftol_abs

$opt->set_ftol_abs( $tol );

set_ftol_rel

$opt->set_ftol_rel( $tol );

set_initial_step

$opt->set_initial_step(\@dx);

@dx has length $n.

set_initial_step1

$opt->set_initial_step1( $dx );

set_local_optimizer

$opt->set_local_optmizer( $local_opt );

set_lower_bound

$opt->set_lower_bound( $i, $ub );

Set the lower bound for parameter $i (zero based) to $ub

set_lower_bounds

$opt->set_lower_bounds(\@ub);

@ub has length $n.

set_lower_bounds1

$opt->set_lower_bounds1 ($ub);

set_max_objective

$opt->set_max_objective( \&func, ?$data );

See "Objective Functions"

set_maxeval

$opt->set_maxeval( $max_iterations );

set_maxtime

$opt->set_maxtime( $time );

set_min_objective

$opt->set_min_objective( \&func, ?$data );

See "Objective Functions"

set_param

$opt->set_param( $name, $value );

set_population

$opt->set_population( $pop );

set_precond_max_objective

$opt->set_precond_max_objective( \&func, \&precond, ?$data);

See "Preconditioned Objectives"

set_precond_min_objective

$opt->set_precond_min_objective( \&func, \&precond, ?$data);

See "Preconditioned Objectives"

set_stopval

$opt->set_stopval( $stopval);

set_upper_bound

$opt->set_upper_bound( $i, $ub );

Set the upper bound for parameter $i (zero based) to $ub

set_upper_bounds

$opt->set_upper_bounds(\@ub);

@ub has length $n.

set_upper_bounds1

$opt->set_upper_bounds1 ($ub);

set_vector_storage

$opt->set_vector_storage( $dim )

set_x_weights

$opt->set_x_weights( \@weights );

@weights has length $n.

set_x_weights1

$opt->set_x_weights1( $weight );

set_xtol_abs

$opt->set_xtol_abs( \@tol );

@tol has length $n.

set_xtol_abs1

$opt->set_xtol_abs1( $tol );

set_xtol_rel

$opt->set_xtol_rel( $tol );

new

my $opt = Math::NLopt->new( $algorithm, $n );

Create an optimization object for the given algorithm and number of parameters. $algorith is one of the algorithm constants, e.g.

use Math::NLopt 'NLOPT_LD_MMA';
my $opt = Math::NLopt->new( NLOPT_LD_MMA, 3 );

INTERNALS

CONSTRUCTORS

UTILITY SUBROUTINES

These are exportable individually, or en-masse via the :utils tag, but beware that srand has same name as the Perl srand routine, and version is rather generic.

algorithm_from_string

$algorithm_int_id = algorithm_from_string( $algorithm_string_id );

return an integer id (e.g. NLOPT_LD_MMA) from a string id (e.g. 'LD_MMA').

algorithm_name

$algorithm_name = algorithm_from_string( $algorithm_int_id );

return a descriptive name from an integer id

algorithm_to_string

$algorithm_string_id = algorithm_to_string( $algorithm_int_id );

result_from_string

$result_int_id = result_from_string( $result_string_id );

return an integer id (e.g. NLOPT_SUCCESS) from a string id (e.g. 'SUCCESS').

result_to_string

$result_string_id = result_to_string( $result_int_id );

srand

srand( $seed )

srand_time

version

($major, $minor, $bugfix ) = Math::NLopt::version()

SUPPORT

Bugs

Please report any bugs or feature requests to bug-math-nlopt@rt.cpan.org or through the web interface at: https://rt.cpan.org/Public/Dist/Display.html?Name=Math-NLopt

Source

Source is available at

https://gitlab.com/djerius/math-nlopt

and may be cloned from

https://gitlab.com/djerius/math-nlopt.git

AUTHOR

Diab Jerius <djerius@cpan.org>

COPYRIGHT AND LICENSE

This is free software, licensed under:

The GNU General Public License, Version 3, June 2007

To install Math::NLopt, copy and paste the appropriate command in to your terminal.

cpanm

cpanm Math::NLopt

CPAN shell

perl -MCPAN -e shell
install Math::NLopt

For more information on module installation, please visit the detailed CPAN module installation guide.

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