This talk will be an introduction to doing scientific computing with Perl and Math::GSL.
* features:
** numerical derivatives and integration
** special functions, statistics, permutations/combinations
** random number generators
** Linear algebra (BLAS)
** Fast Fourier Transform
** Wavelets
** Splines/Interpolation
** Histograms
** 1D Curve Fitting
** 1D Root Solving
** Multidim. Root Solving (soon)
** Ordinary Differential Equation Solver (soon)
** Multidimensional curvefitting (soon)
** Multidimensional minimization (soon)
* use Perl+Math::GSL as glue between sensors/data source and computer algebra system (like Matlab/Mathematica/Maple/Macsyma ... )
** why isn't there a Perl CAS or something like sciPy.org ?
* why it is better than pure Perl
** 2.5x faster numeric sort, sort_k_largest, sort_k_smallest functions
* why Math::GSL + Perl beats "whatever you're doing now"
** throwaway analysis code is 80% data translation, 20% heavy-lifting
** easier to work with sensor data, networking
** somewhere between matlab and a monolithic fortran solver on a cluster of mainframes
[http://podasp.com/episodes/P/PD/PDX.pm/2385/20080910__MathGSL_Scientific_Comp__2385.mp3 Podcast] [http://leto.net/gitweb/?p=presentations.git;a=blob_plain;f=ScientificComputingWithPerlAndMathGSL/pres.pdf Slides]