Search Results for "programming c"

...The source is written in literate programming NoWeb.

...Because of a high-level schematic-description language -- in form of provided Scilab commands -- only a minimal implementation-effort (no error-prone C-programming) is required.

The toolkit glpk supports methods for mixed integer linear programming (MILP). These methods solve Capital Budgeting Problems (CBP). Unfortunately, glpk does not support any multithreading and there is no feature to distribute problems via network connections. Today, this is a pitiable sight, because modern computer systems are coupled by networks and support multi threading. We create a distributed system with Apache thrift and the C-API of glpk.

OpenAxiom is an open source Computer Algebra System. It offers an interactive environment, an expressive programming language, a compiler, a large set of mathematical libraries of interest to researchers and practitioners of computational sciences.

This project aims to build a portable pure C++ framework for GPGPU operations. It is conceived to ease scientific researches that need to speed up algebric and calculus operations on normal hardware, without the need of graphic programming knowledge.

Path Partitioner constructs random graph partitions and solves the linear (or integer) programming problems that assign weights to the edges such that the performance ratio of the local search algorithm for the Path Partitioning problem is minimized. See http://www.tau.ac.il/~ohadschn/Thesis/Partitioning.pdf. Requirements: Microsoft Automatic Graph Layout http://research.microsoft.com/en-us/projects/msagl/ Gurobi Optimizer http://www.gurobi.com/ Permutations, Combinations, and Variations using C# Generics http://www.codeproject.com/Articles/26050/Permutations-Combinations-and-Variations-using-C-G

DBNL is a cross-platform library that offers a variety of implementations of Bayesian networks and machine learning algorithms. It is a flexible library that covers all aspects of Bayesian netwoks from representation to reasoning and learning. It allows you to create simple static networks as well as complex temporal models with changing structure. It can handle highly non-linear dependencies between multivariate random variables. The particle based inference can answer arbitrary...