COIN-OR
The Computational Infrastructure for Operations Research (COIN-OR**, or simply COIN) project is an initiative to spur the development of open-source software for the operations research community. Why open source? The Open Source Initiative explains it well. When people can read, redistribute, and modify the source code, software evolves. People improve it, people adapt it, people fix bugs. The results of open-source development have been remarkable. Community-based efforts to develop software under open-source licenses have produced high-quality, high-performance code---code on which much of the Internet is run. Why for OR? Consider the following scenario. You read about an optimization algorithm in the literature and you get an idea on how to improve it. Today, testing your new idea typically requires re-implementing (and re-debugging and re-testing) the original algorithm. Often, clever implementation details aren’t published. It can be difficult to replicate reported performance. Now imagine the scenario if the original algorithm was publicly available in a community repository. Weeks of re-implementing would no longer be required. You would simply check out a copy of it for yourself and modify it. Imagine the productivity gains from software reuse!
Keywords for this software
References in zbMATH (referenced in 98 articles )
Showing results 1 to 20 of 98.
Sorted by year (- Coey, Chris; Lubin, Miles; Vielma, Juan Pablo: Outer approximation with conic certificates for mixed-integer convex problems (2020)
- Gleixner, Ambros; Maher, Stephen J.; Müller, Benjamin; Pedroso, João Pedro: Price-and-verify: a new algorithm for recursive circle packing using Dantzig-Wolfe decomposition (2020)
- Kazachkov, Aleksandr M.; Nadarajah, Selvaprabu; Balas, Egon; Margot, François: Partial hyperplane activation for generalized intersection cuts (2020)
- Liberti, Leo: Distance geometry and data science (2020)
- Müller, Benjamin; Serrano, Felipe; Gleixner, Ambros: Using two-dimensional projections for stronger separation and propagation of bilinear terms (2020)
- Neumann, Christoph; Stein, Oliver; Sudermann-Merx, Nathan: Granularity in nonlinear mixed-integer optimization (2020)
- Altmanová, Katerina; Knop, Dusan; Koutecký, Martin: Evaluating and tuning (n)-fold integer programming (2018)
- Bell, Bradley M.; Kristensen, Kasper: Newton step methods for AD of an objective defined using implicit functions (2018)
- Berthold, Timo; Hendel, Gregor; Koch, Thorsten: From feasibility to improvement to proof: three phases of solving mixed-integer programs (2018)
- Corrêa, Ricardo C.; Donne, Diego Delle; Koch, Ivo; Marenco, Javier: General cut-generating procedures for the stable set polytope (2018)
- Diarrassouba, Ibrahima; Labidi, Mohamed Khalil; Mahjoub, Ali Ridha: A hybrid optimization approach for the Steiner (k)-connected network design problem (2018)
- Huangfu, Q.; Hall, J. A. J.: Parallelizing the dual revised simplex method (2018)
- Kılınç, Mustafa R.; Sahinidis, Nikolaos V.: Exploiting integrality in the global optimization of mixed-integer nonlinear programming problems with BARON (2018)
- Lancia, Giuseppe; Serafini, Paolo: Compact extended linear programming models (2018)
- Aldasoro, Unai; Escudero, Laureano F.; Merino, María; Pérez, Gloria: A parallel branch-and-fix coordination based matheuristic algorithm for solving large sized multistage stochastic mixed 0-1 problems (2017)
- Belotti, Pietro; Berthold, Timo: Three ideas for a feasibility pump for nonconvex MINLP (2017)
- Camm, Jeffrey D.; Magazine, Michael J.; Kuppusamy, Saravanan; Martin, Kipp: The demand weighted vehicle routing problem (2017)
- D’Ambrosio, Claudia; Vu, Ky; Lavor, Carlile; Liberti, Leo; Maculan, Nelson: New error measures and methods for realizing protein graphs from distance data (2017)
- Gleixner, Ambros M.; Berthold, Timo; Müller, Benjamin; Weltge, Stefan: Three enhancements for optimization-based bound tightening (2017)
- Hart, William E.; Laird, Carl D.; Watson, Jean-Paul; Woodruff, David L.; Hackebeil, Gabriel A.; Nicholson, Bethany L.; Siirola, John D.: Pyomo -- optimization modeling in Python (2017)