The general purpose of this library is to provide C language procedures related
to cartographic processes. Procedures for each of the processes will be strictly
categorized and although they may share common subfunctions they will not
intersect in scope.
Msieve is a library and utility for factoring large integers using the most
powerful modern algorithms. It features a stable and very fast implementation
of a self-initializing multiple polynomial quadratic sieve (MPQS), plus a
somewhat experimental general number field sieve (GNFS) implementation.
Primary design goals are speed, portability and ease of use. Msieve claims to
be the fastest implementation for factoring general inputs between 40 and 100
decimal digits, but can handle larger input as well.
ocamlgsl is an interface to GSL (GNU scientific library), for the
Objective Caml langage.
Supports base-2, base-10, base-16, and base-256 numbers.
Uses the GMP or BCMath extensions, if available,
and an internal implementation, otherwise.
A package that returns all the combinations and
permutations, without repitition, of a given set
and subset size. Associative arrays are preserved.
This is a port of Phil Karn's Reed-Solomon CODEC library. This package may be
useful to programmers working on data communications software.
This is a port of Phil Karn's SIMD assisted Viterbi CODEC library. This
package may be useful to programmers working on data communications software.
The symeig module contains a Python wrapper for the LAPACK functions to
solve the standard and generalized eigenvalue problems for symmetric
(hermitian) positive definite matrices. Those specialized algorithms give
an important speed-up with respect to the generic LAPACK eigenvalue
problem solver used by NumPy (linalg.eig and linalg.eigh).
sfft is a library to compute discrete Fourier transforms of signals with
a sparse frequency domain, using an algorithm that is more efficient than
other known FFT algorithms. It was developed by Haitham Hassanieh, Piotr
Indyk, Dina Katabi, and Eric Price at the Computer Science and Artifical
Intelligence Lab at MIT. Performance optimizations were developed by J.
Schumacher at the Computer Science Department of ETH Zurich in 2013.
This program demonstrates the working principles of some 20 sorting
algorithms and is very easy to use. Select the algorithm, the speed
of the visualisation and whether you want to get sound output (that
reflects the values being moved and is characteristic for each kind
of algorithm).