Skip to content

yojvr/or-tools

 
 

Repository files navigation

OR-Tools - Google Optimization Tools

PyPI version PyPI download Binder
NuGet version NuGet download
Maven Central
Discord

Google's software suite for combinatorial optimization.

Table of Contents

About OR-Tools

Google Optimization Tools (a.k.a., OR-Tools) is an open-source, fast and portable software suite for solving combinatorial optimization problems.

The suite contains:

  • Two constraint programming solver (CP* and CP-SAT);
  • Two linear programming solvers (Glop and PDLP);
  • Wrappers around commercial and other open source solvers, including mixed integer solvers;
  • Bin packing and knapsack algorithms;
  • Algorithms for the Traveling Salesman Problem and Vehicle Routing Problem;
  • Graph algorithms (shortest paths, min cost flow, max flow, linear sum assignment).

We wrote OR-Tools in C++, but provide wrappers in Python, C# and Java.

Codemap

This software suite is composed of the following components:

  • Makefile Top-level for GNU Make based build.
  • makefiles Subsidiary Make files, CI and build system documentation.
  • CMakeLists.txt Top-level for CMake based build.
  • cmake Subsidiary CMake files, CI and build system documentation.
  • WORKSPACE Top-level for Bazel based build.
  • bazel Subsidiary Bazel files, CI and build system documentation.
  • ortools Root directory for source code.
    • base Basic utilities.
    • algorithms Basic algorithms.
      • samples Carefully crafted samples.
    • graph Graph algorithms.
      • samples Carefully crafted samples.
    • linear_solver Linear solver wrapper.
      • samples Carefully crafted samples.
    • glop Simplex-based linear programming solver.
      • samples Carefully crafted samples.
    • pdlp First-order linear programming solver.
      • samples Carefully crafted samples.
    • lp_data Data structures for linear models.
    • constraint_solver Constraint and Routing solver.
      • docs Documentation of the component.
      • samples Carefully crafted samples.
    • sat SAT solver.
      • docs Documentation of the component.
      • samples Carefully crafted samples.
    • bop Boolean solver based on SAT.
    • util Utilities needed by the constraint solver
  • examples Root directory for all examples.
  • tools Delivery Tools (e.g. Windows GNU binaries, scripts, release dockers)

Installation

This software suite has been tested under:

  • Ubuntu 18.04 LTS and up (64-bit);
  • Apple macOS Mojave with Xcode 9.x (64-bit);
  • Microsoft Windows with Visual Studio 2022 (64-bit).

OR-Tools currently builds with a Makefile, but also provides Bazel and CMake support.

For installation instructions (both source and binary), please visit https://developers.google.com/optimization/introduction/installing.

Build from source using Make (legacy)

We provide a Make based build.
Please check the Make build instructions.

Build from source using CMake

We provide a CMake based build.
Please check the CMake build instructions.

Build from source using Bazel

We provide a Bazel based build.
Please check the Bazel build instructions.

Quick Start

The best way to learn how to use OR-Tools is to follow the tutorials in our developer guide:

https://developers.google.com/optimization/introduction/get_started

If you want to learn from code examples, take a look at the examples in the examples directory.

Documentation

The complete documentation for OR-Tools is available at: https://developers.google.com/optimization/

Contributing

The CONTRIBUTING.md file contains instructions on how to submit the Contributor License Agreement before sending any pull requests (PRs). Of course, if you're new to the project, it's usually best to discuss any proposals and reach consensus before sending your first PR.

License

The OR-Tools software suite is licensed under the terms of the Apache License 2.0.
See LICENSE for more information.

Releases

No releases published

Packages

No packages published

Languages

  • C++ 76.0%
  • Python 7.6%
  • Julia 3.0%
  • C# 2.8%
  • Java 2.7%
  • Starlark 1.8%
  • Other 6.1%