math::derivative(3pm) [debian man page]

Derivative(3pm) 					User Contributed Perl Documentation					   Derivative(3pm)

NAME

       Math::Derivative - Numeric 1st and 2nd order differentiation

SYNOPSIS

	   use Math::Derivative qw(Derivative1 Derivative2);
	   @dydx=Derivative1(@x,@y);
	   @d2ydx2=Derivative2(@x,@y);
	   @d2ydx2=Derivative2(@x,@y,$yp0,$ypn);

DESCRIPTION

       This Perl package exports functions for performing numerical first (Derivative1) and second Derivative2) order differentiation on vectors
       of data. They both take references to two arrays containing the x and y ordinates of the data and return an array of the 1st or 2nd
       derivative at the given x ordinates. Derivative2 may optionally be given values to use for the first dervivative at the start and end
       points of the data - otherwiswe 'natural' values are used.

HISTORY

       $Log: Derivative.pm,v $ Revision 1.1  1995/12/26 16:26:59  willijar Initial revision

BUGS

       Let me know.

AUTHOR

       John A.R. Williams <J.A.R.Williams@aston.ac.uk>

perl v5.12.3							    2011-06-14							   Derivative(3pm)

Math::Symbolic::Derivative(3pm) 			User Contributed Perl Documentation			   Math::Symbolic::Derivative(3pm)

NAME

       Math::Symbolic::Derivative - Derive Math::Symbolic trees

SYNOPSIS

	 use Math::Symbolic::Derivative qw/:all/;
	 $derived = partial_derivative($term, $variable);
	 # or:
	 $derived = total_derivative($term, $variable);

DESCRIPTION

       This module implements derivatives for Math::Symbolic trees.  Derivatives are Math::Symbolic::Operators, but their implementation is drawn
       from this module because it is significantly more complex than the implementation of most operators.

       Derivatives come in two flavours. There are partial- and total derivatives.

       Explaining the precise difference between partial- and total derivatives is beyond the scope of this document, but in the context of
       Math::Symbolic, the difference is simply that partial derivatives just derive in terms of explicit dependency on the differential variable
       while total derivatives recongnize implicit dependencies from variable signatures.

       Partial derivatives are faster, have been tested more thoroughly, and are probably what you want for simpler applications anyway.

   EXPORT
       None by default. But you may choose to import the total_derivative() and partial_derivative() functions.

CLASS DATA

       The package variable %Partial_Rules contains partial derivative rules as key-value pairs of names and subroutines.

SUBROUTINES

   partial_derivative
       Takes a Math::Symbolic tree and a Math::Symbolic::Variable as argument.	third argument is an optional boolean indicating whether or not
       the tree has to be cloned before being derived. If it is true, the subroutine happily stomps on any code that might rely on any components
       of the Math::Symbolic tree that was passed to the sub as first argument.

   total_derivative
       Takes a Math::Symbolic tree and a Math::Symbolic::Variable as argument.	third argument is an optional boolean indicating whether or not
       the tree has to be cloned before being derived. If it is true, the subroutine happily stomps on any code that might rely on any components
       of the Math::Symbolic tree that was passed to the sub as first argument.

AUTHOR

       Please send feedback, bug reports, and support requests to the Math::Symbolic support mailing list: math-symbolic-support at lists dot
       sourceforge dot net. Please consider letting us know how you use Math::Symbolic. Thank you.

       If you're interested in helping with the development or extending the module's functionality, please contact the developers' mailing list:
       math-symbolic-develop at lists dot sourceforge dot net.

       List of contributors:

	 Steffen Mueller, symbolic-module at steffen-mueller dot net
	 Stray Toaster, mwk at users dot sourceforge dot net
	 Oliver Ebenhoeh

SEE ALSO

       New versions of this module can be found on http://steffen-mueller.net or CPAN. The module development takes place on Sourceforge at
       http://sourceforge.net/projects/math-symbolic/

       Math::Symbolic

perl v5.10.1							    2011-01-01					   Math::Symbolic::Derivative(3pm)