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The Matrix Template Library (MTL) is a high-performance generic component library that provides comprehensive linear algebra functionality for a wide variety of matrix formats. As with the Standard Template Library (STL), MTL uses a five-fold approach, consisting of generic functions, containers, iterators, adaptors, and function objects, all developed specifically for high performance numerical linear algebra. Within this framework, MTL provides generic algorithms corresponding to the mathematical operations that define linear algebra. Similarly, the containers, adaptors, and iterators are used to represent and to manipulate concrete linear algebra objects such as matrices and vectors.

The octave-forge package is the result of The GNU Octave Repositry project, which is intended to be a central location for custom scripts, functions and extensions for GNU Octave. contains the source for all the functions plus build and install scripts. This is generate_html. This package provides functions for generating HTML pages that contain the help texts for a set of functions. The package is designed to be as general as possible, but also contains convenience functions for generating a set of pages for entire packages.

OGDF (Open Graph Drawing Framework) is a self-contained C++ class library for the automatic layout of diagrams. OGDF offers sophisticated algorithms and data structures to use within your own applications or scientific projects. The library provides: * A wide range of graph drawing algorithms that allow to reuse and replace particular algorithm phases by using a dedicated module mechanism. * Sophisticated data structures that are commonly used in graph drawing, equipped with rich public interfaces. * Self-contained code that does not require any additional libraries (except for some optional branch-and-cut algorithms).

Math::GMPq is a bigrational module utilising the GNU MP (GMP) library. Basically this module simply wraps all of the mpq rational functions provided by that library. See the Math::GMPq test suite for some examples of usage. IMPORTANT: If your perl was built with '-Duse64bitint' you need to assign all integers larger than 52-bit in a 'use integer;' block. Failure to do so can result in the creation of the variable as an NV (rather than an IV) - with a resultant loss of precision.

This package supplies a set of functions that find the roots of polynomials. Polynomials up to the quartic may be solved directly by numerical formulae. Polynomials of fifth and higher powers will be solved by an iterative method, as there are no general solutions for fifth and higher powers. The linear, quadratic, cubic, and quartic *_roots() functions all expect to have a non-zero value for the $a term. If the constant term is zero then the first value returned in the list of answers will always be zero, for all functions.

This module is an extension to the Math::Symbolic module. A basic familiarity with that module is required. The Math::Symbolic::Custom::Pattern module implements pattern matching routines on Math::Symbolic trees. The patterns itself are constructed from Math::Symbolic trees with just a few variables which have a special meaning. The module provides two interfaces. You can use the new() and match() methods this class provides, or you can use the to_pattern() and is_of_form() methods on any Math::Symbolic tree. (Exported by the Math::Symbolic::Custom::Pattern::Export module. Refer to that module for details on is_of_form().)

The cdecimal is a fast drop-in replacement for the decimal module in Python's standard library. Both modules provide complete implementations of the General Decimal Arithmetic Specification. Typical performance gains are between 30x for I/O heavy benchmarks and 80x for numerical programs. In a database benchmark, cdecimal exhibits a speedup of 12x over decimal.py. decimal cdecimal speedup pi 42.75s 0.58s 74x telco 172.19s 5.68s 30x psycopg 3.57s 0.29s 12x All Python versions from 2.5 up to 3.2 are supported. For the few remaining differences, read the cdecimal documentation. cdecimal has been included in Python-3.3.

TetGen is a program for generating tetrahedral meshes for arbitrary 3D domains. The main purpose of TetGen is to create high-quality tetrahedral meshes for solving partial differential equations using finite element and finite volume methods. This program, based on Delaunay methods, currently generates meshes including exact constrained Delaunay tetrahedralizations and quality (conforming Delaunay) meshes. For a 3D point set, it generates its exact Delaunay tetrahedralization and convex hull as well. The program is written in ANSI C++. It is highly portable, it should be very easy to compile and run on all major computer systems. It can be also used as a library embedded into other applications.

Yacas (Yet Another Computer Algebra System) is a small and highly flexible general-purpose computer algebra language. The syntax uses a infix-operator grammar parser. The distribution contains a small library of mathematical functions, but its real strength is in the language in which you can easily write your own symbolic manipulation algorithms. The core engine supports arbitrary precision arithmetic (although it can optionally be linked with the library "libgmp3") and is able to execute symbolic manipulations on various mathematical objects by following user-defined rules.

From README: Aclgen is a tool to create minimal length ip access lists and aggregated routing tables primarily for Cisco routers. Aclgen is fast. It takes only 15 seconds to create a 17000 line long access list for some 44000 entries of my routing table. Aclgen is flexible. It accepts various input formats and you can specify any output format by a printf like format-string. Aclgen is intended to use in large scripts creating router configuration from different databases. Any contribution is welcome. Send me your script using aclgen. I'll bundle it with the program.