Provided by: libmath-gsl-perl_0.43-4_amd64 
NAME
Math::GSL::ODEIV - functions for solving ordinary differential equation (ODE) initial
value problems
SYNOPSIS
use Math::GSL::ODEIV qw /:all/;
DESCRIPTION
Here is a list of all the functions in this module :
• "gsl_odeiv_step_alloc($T, $dim)" - This function returns a pointer to a newly
allocated instance of a stepping function of type $T for a system of $dim
dimensions.$T must be one of the step type constant above.
• "gsl_odeiv_step_reset($s)" - This function resets the stepping function $s. It should
be used whenever the next use of s will not be a continuation of a previous step.
• "gsl_odeiv_step_free($s)" - This function frees all the memory associated with the
stepping function $s.
• "gsl_odeiv_step_name($s)" - This function returns a pointer to the name of the
stepping function.
• "gsl_odeiv_step_order($s)" - This function returns the order of the stepping function
on the previous step. This order can vary if the stepping function itself is adaptive.
• "gsl_odeiv_step_apply "
• "gsl_odeiv_control_alloc($T)" - This function returns a pointer to a newly allocated
instance of a control function of type $T. This function is only needed for defining
new types of control functions. For most purposes the standard control functions
described above should be sufficient. $T is a gsl_odeiv_control_type.
• "gsl_odeiv_control_init($c, $eps_abs, $eps_rel, $a_y, $a_dydt) " - This function
initializes the control function c with the parameters eps_abs (absolute error),
eps_rel (relative error), a_y (scaling factor for y) and a_dydt (scaling factor for
derivatives).
• "gsl_odeiv_control_free "
• "gsl_odeiv_control_hadjust "
• "gsl_odeiv_control_name "
• "gsl_odeiv_control_standard_new($eps_abs, $eps_rel, $a_y, $a_dydt)" - The standard
control object is a four parameter heuristic based on absolute and relative errors
$eps_abs and $eps_rel, and scaling factors $a_y and $a_dydt for the system state y(t)
and derivatives y'(t) respectively. The step-size adjustment procedure for this method
begins by computing the desired error level D_i for each component, D_i = eps_abs +
eps_rel * (a_y |y_i| + a_dydt h |y'_i|) and comparing it with the observed error E_i =
|yerr_i|. If the observed error E exceeds the desired error level D by more than 10%
for any component then the method reduces the step-size by an appropriate factor,
h_new = h_old * S * (E/D)^(-1/q) where q is the consistency order of the method (e.g.
q=4 for 4(5) embedded RK), and S is a safety factor of 0.9. The ratio E/D is taken to
be the maximum of the ratios E_i/D_i. If the observed error E is less than 50% of the
desired error level D for the maximum ratio E_i/D_i then the algorithm takes the
opportunity to increase the step-size to bring the error in line with the desired
level, h_new = h_old * S * (E/D)^(-1/(q+1)) This encompasses all the standard error
scaling methods. To avoid uncontrolled changes in the stepsize, the overall scaling
factor is limited to the range 1/5 to 5.
• "gsl_odeiv_control_y_new($eps_abs, $eps_rel)" - This function creates a new control
object which will keep the local error on each step within an absolute error of
$eps_abs and relative error of $eps_rel with respect to the solution y_i(t). This is
equivalent to the standard control object with a_y=1 and a_dydt=0.
• "gsl_odeiv_control_yp_new($eps_abs, $eps_rel)" - This function creates a new control
object which will keep the local error on each step within an absolute error of
$eps_abs and relative error of $eps_rel with respect to the derivatives of the
solution y'_i(t). This is equivalent to the standard control object with a_y=0 and
a_dydt=1.
• "gsl_odeiv_control_scaled_new($eps_abs, $eps_rel, $a_y, $a_dydt, $scale_abs, $dim) " -
This function creates a new control object which uses the same algorithm as
gsl_odeiv_control_standard_new but with an absolute error which is scaled for each
component by the array reference $scale_abs. The formula for D_i for this control
object is, D_i = eps_abs * s_i + eps_rel * (a_y |y_i| + a_dydt h |y'_i|) where s_i is
the i-th component of the array scale_abs. The same error control heuristic is used by
the Matlab ode suite.
• "gsl_odeiv_evolve_alloc($dim)" - This function returns a pointer to a newly allocated
instance of an evolution function for a system of $dim dimensions.
• "gsl_odeiv_evolve_apply($e, $c, $step, $dydt, \$t, $t1, \$h, $y)" - This function
advances the system ($e, $dydt) from time $t and position $y using the stepping
function $step. The new time and position are stored in $t and $y on output. The
initial step-size is taken as $h, but this will be modified using the control function
$c to achieve the appropriate error bound if necessary. The routine may make several
calls to step in order to determine the optimum step-size. If the step-size has been
changed the value of $h will be modified on output. The maximum time $t1 is guaranteed
not to be exceeded by the time-step. On the final time-step the value of $t will be
set to $t1 exactly.
• "gsl_odeiv_evolve_reset($e)" - This function resets the evolution function $e. It
should be used whenever the next use of $e will not be a continuation of a previous
step.
• "gsl_odeiv_evolve_free($e)" - This function frees all the memory associated with the
evolution function $e.
This module also includes the following constants :
• $GSL_ODEIV_HADJ_INC
• $GSL_ODEIV_HADJ_NIL
• $GSL_ODEIV_HADJ_DEC
Step Type
• $gsl_odeiv_step_rk2 - Embedded Runge-Kutta (2, 3) method.
• $gsl_odeiv_step_rk4 - 4th order (classical) Runge-Kutta. The error estimate is
obtained by halving the step-size. For more efficient estimate of the error, use the
Runge-Kutta-Fehlberg method described below.
• $gsl_odeiv_step_rkf45 - Embedded Runge-Kutta-Fehlberg (4, 5) method. This method is a
good general-purpose integrator.
• $gsl_odeiv_step_rkck - Embedded Runge-Kutta Cash-Karp (4, 5) method.
• $gsl_odeiv_step_rk8pd - Embedded Runge-Kutta Prince-Dormand (8,9) method.
• $gsl_odeiv_step_rk2imp - Implicit 2nd order Runge-Kutta at Gaussian points.
• $gsl_odeiv_step_rk2simp
• $gsl_odeiv_step_rk4imp - Implicit 4th order Runge-Kutta at Gaussian points.
• $gsl_odeiv_step_bsimp - Implicit Bulirsch-Stoer method of Bader and Deuflhard. This
algorithm requires the Jacobian.
• $gsl_odeiv_step_gear1 - M=1 implicit Gear method.
• $gsl_odeiv_step_gear2 - M=2 implicit Gear method.
For more information on the functions, we refer you to the GSL official documentation:
<http://www.gnu.org/software/gsl/manual/html_node/>
EXAMPLE
The example is taken from <https://www.math.utah.edu/software/gsl/gsl-ref_367.html>.
use strict;
use warnings;
use Math::GSL::Errno qw($GSL_SUCCESS);
use Math::GSL::ODEIV qw/ :all /;
use Math::GSL::Matrix qw/:all/;
use Math::GSL::IEEEUtils qw/ :all /;
sub func {
my ($t, $y, $dydt, $params) = @_;
my $mu = $params->{mu};
$dydt->[0] = $y->[1];
$dydt->[1] = -$y->[0] - $mu*$y->[1]*(($y->[0])**2 - 1);
return $GSL_SUCCESS;
}
sub jac {
my ($t, $y, $dfdy, $dfdt, $params) = @_;
my $mu = $params->{mu};
my $m = gsl_matrix_view_array($dfdy, 2, 2);
gsl_matrix_set( $m, 0, 0, 0.0 );
gsl_matrix_set( $m, 0, 1, 1.0 );
gsl_matrix_set( $m, 1, 0, (-2.0 * $mu * $y->[0] * $y->[1]) - 1.0 );
gsl_matrix_set( $m, 1, 1, -$mu * (($y->[0])**2 - 1.0) );
$dfdt->[0] = 0.0;
$dfdt->[1] = 0.0;
return $GSL_SUCCESS;
}
my $T = $gsl_odeiv_step_rk8pd;
my $s = gsl_odeiv_step_alloc($T, 2);
my $c = gsl_odeiv_control_y_new(1e-6, 0.0);
my $e = gsl_odeiv_evolve_alloc(2);
my $params = { mu => 10 };
my $sys = Math::GSL::ODEIV::gsl_odeiv_system->new(\&func, \&jac, 2, $params );
my $t = 0.0;
my $t1 = 100.0;
my $h = 1e-6;
my $y = [ 1.0, 0.0 ];
gsl_ieee_env_setup;
while ($t < $t1) {
my $status = gsl_odeiv_evolve_apply ($e, $c, $s, $sys, \$t, $t1, \$h, $y);
last if $status != $GSL_SUCCESS;
printf "%.5e %.5e %.5e\n", $t, $y->[0], $y->[1];
}
gsl_odeiv_evolve_free($e);
gsl_odeiv_control_free($c);
gsl_odeiv_step_free($s);
AUTHORS
Jonathan "Duke" Leto <jonathan@leto.net> and Thierry Moisan <thierry.moisan@gmail.com>
COPYRIGHT AND LICENSE
Copyright (C) 2008-2021 Jonathan "Duke" Leto and Thierry Moisan
This program is free software; you can redistribute it and/or modify it under the same
terms as Perl itself.