Search Results for "mixed integer linear programming"

X11-Basic is a dialect of the BASIC programming language with graphics capability that integrates features like shell scripting, cgi-Programming and full graphical visualisation into the easy to learn basic language on modern computers.

Mixed Integer Linear Programming (MILP) solver lp_solve solves pure linear, (mixed) integer/binary, semi-cont and special ordered sets (SOS) models.lp_solve is written in ANSI C and can be compiled on many different platforms like Linux and WINDOWS This project is moved to github: https://lp-solve.github.io/

GLPK for Java provides a Java language binding for the library GLPK. GLPK is a proven solver for linear and mixed integer mathematical programming problems. For Windows binaries see project GLPK for Windows (http://sourceforge.net/projects/winglpk/).

Arageli is a C++ library for computations in arithmetic, algebra, geometry, linear and integer linear programming. Arageli provides routines and classes that support precise, i.e. symbolic or algebraic, computations.

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.

OCaml bindings for the GLPK (GNU Linear Programming Kit) library for solving linear programming and mixed integer programming problems.

This is a series of *.java Classes for making programming tasks simpler, much like Apache Commons. Included in this src package is an Advanced Randomizer, TStringList Clone From Delphi, an Algorithem Class, and Method MultiTask Better than Runnables.

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...