Search Results for "mixed integer linear programming"
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Showing 18 open source projects for "mixed integer linear programming"
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OR-Tools - Google Optimization Tools
Google's software suite for combinatorial optimization
Google Optimization Tools, also known as OR-Tools is an open-source, fast and portable software suite for solving combinatorial optimization problems. These encompass problems in vehicle routing, flows, integer and linear programming, and constraint programming. This suite contains a number of solvers, namely: a constraint programming solver; a linear programming solver; wrappers for commercial solvers (like Gurobi or CPLEX) and other open source solvers (SCIP, GLPK, etc.); among others. OR-Tools is written in C++, but comes with wrappers for Python, C# and Java. ... -
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Convex.jl
A Julia package for disciplined convex programming
Convex.jl is a Julia package for Disciplined Convex Programming (DCP). Convex.jl makes it easy to describe optimization problems in a natural, mathematical syntax, and to solve those problems using a variety of different (commercial and open-source) solvers. Convex.jl works by transforming the problem—which possibly has nonsmooth, nonlinear constructions like the nuclear norm, the log determinant, and so forth—into a linear optimization problem subject to conic constraints. This... -
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InferOpt.jl
Combinatorial optimization layers for machine learning pipelines
InferOpt.jl is a toolbox for using combinatorial optimization algorithms within machine learning pipelines. It allows you to create differentiable layers from optimization oracles that do not have meaningful derivatives. Typical examples include mixed integer linear programs or graph algorithms. -
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NVIDIA cuOpt
GPU accelerated decision optimization
NVIDIA cuOpt is a GPU-accelerated optimization engine designed to solve complex mathematical optimization problems at large scale. It supports a range of optimization models including linear programming (LP), mixed integer linear programming (MILP), quadratic programming (QP), and vehicle routing problems (VRP). Built primarily in C++, cuOpt leverages NVIDIA GPUs to deliver near real-time solutions for optimization tasks involving millions of variables and constraints. The platform provides multiple interfaces, including C, Python, and server APIs, allowing developers to integrate optimization capabilities into applications and services. cuOpt is designed for high-performance environments and can be deployed across cloud, hybrid, or on-premise infrastructures. ... -
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Data Envelopment Analysis using Stata
Develop a user written Data Envelopment Analysis package in Stata.
The goal of this project is to develop a Data Envelopment Analysis(DEA) program using Stata programming language. This is the replacement of "deastata" project that we maintained for the purpose of version management. We call the program package "DEAS" which stands for Data Envelopment Analysis using Stata. DEAS covers the basic models of DEA and extensions including CCR, BCC, SBM, Super-efficiency Model, Allocative Model(Profit, Revenue, Cost), (Global) Malmquist Productivity Index Model, Imprecise DEA, FDH, Additive Model, Virtual Price Model, linear programming(lp), mixed integer linear programming(MILP), and more... -
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X11-Basic
A BASIC interpreter and compiler with graphics.
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. -
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lpsolve
Mixed Integer Linear Programming (MILP) solver.
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/ -
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An open source Solver-compatible optimization engine for Microsoft Excel on Windows and Mac using the Coin-OR CBC linear and integer programming optimizer. You can also use other solvers like Gurobi (if you have it installed), solvers on the NEOS cloud, and various non-linear solvers are supported too.
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GLPK for Java
Java language binding for the GNU Linear Programming Kit (GLPK)
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/). -
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SONNET
SONNET (integer) linear optimisation in .NET
SONNET builds on top of the well-known COIN libraries for optimisation (see http://www.coin-or.org). It brings the power of (integer) linear programming projects of COIN to .NET. SONNET is licensed under the terms of the Eclipse Public License (EPL). SONNET consists of two parts: 1) A .NET library that consists of .NET wrapper classes around existing COIN classes such as OsiSolverInterface. 2) A .NET library implemented in C# that adds a powerful (rudimentary) modelling API with classes like Model, Constraint, Variable, Expression, etc, and overloaded operators to allow for things like Model model = new Model(); Variable x = new Variable(); Variable y = new Variable(); model.Add(2 * x + 3 * y <= 10); model.Objective = 3 * x + y; Solver solver = new Solver(model, typeof(OsiClpSolverInterface)); solver.Maximize(); Console.WriteLine(solver.ToSolutionString()); -
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A java decision supporting application for the urban planning domain. It is designed as a desktop frontend to a Mixed Integer Programming engine. Actually it uses the GNU Linear Programming Kit (GLPK).
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Grammar based Combinatorial Solver
Solver with Applications in Computer Network Systems Design
The solver performs an exhaustive search on the space defined using simple formal language grammar constructs to identify solutions that meet the objective and constraints. It can be used to easily define complex discrete multi-dimensional combinatorial problems that are otherwise difficult to model using Mixed Integer Linear Programming solvers. The solver does not make any assumptions on continuity of the search space. The solver is implemented in PHP. A GoJS based visualization is provided for model's grammar. Two system design model samples are provided. One uses the solver to identify low power network design for Passive Optical Networks given a set of choices for provider and customer premises equipment and a set of optical domain constraints. ... -
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Bat2015
Bachelor of Science (Informatik)
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. -
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Simplex-in-PHP are PHP scripts that solve small linear programming and integer linear programming problems executing the simplex method step by step, printing the tableau and drawing the graphical analysis. Useful for students in Operational Research
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The problem of packing small boxes into a larger box underlies a number of cutting, packing, scheduling, and transportation applications. This solver for the problem, based on integer linear programming relaxations, beats previous work by far.
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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.
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Java ILP is a simple java interface to (mixed) integer linear programming solvers such as lp_solve, Glpk, Gurobi, SAT4J (0-1 ILP), MiniSat+(0-1 ILP), CPLEX, or Mosek. (documentation at http://javailp.sourceforge.net/)
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DBNL
Dynamic Bayesian Network Library
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...
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