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!

References in zbMATH (referenced in 75 articles )

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  1. Belotti, Pietro; Berthold, Timo: Three ideas for a feasibility pump for nonconvex MINLP (2017)
  2. D’Ambrosio, Claudia; Vu, Ky; Lavor, Carlile; Liberti, Leo; Maculan, Nelson: New error measures and methods for realizing protein graphs from distance data (2017)
  3. Gleixner, Ambros M.; Berthold, Timo; Müller, Benjamin; Weltge, Stefan: Three enhancements for optimization-based bound tightening (2017)
  4. 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)
  5. Herrera, Juan F.R.; Salmerón, José M.G.; Hendrix, Eligius M.T.; Asenjo, Rafael; Casado, Leocadio G.: On parallel branch and bound frameworks for global optimization (2017)
  6. Toth, Csaba D. (ed.); Goodman, Jacob E. (ed.); O’Rourke, Joseph (ed.): Handbook of discrete and computational geometry (2017)
  7. Escudero, Laureano F.; Garín, María Araceli; Unzueta, Aitziber: Cluster Lagrangean decomposition in multistage stochastic optimization (2016)
  8. Gassmann, Horand; Ma, Jun; Martin, Kipp: Communication protocols for options and results in a distributed optimization environment (2016)
  9. Lejeune, Miguel A.; Margot, François: Solving chance-constrained optimization problems with stochastic quadratic inequalities (2016)
  10. Nemati, Sepehr; Shylo, Oleg V.; Prokopyev, Oleg A.; Schaefer, Andrew J.: The surgical patient routing problem: a central planner approach (2016)
  11. Santos, Haroldo G.; Toffolo, Túlio A.M.; Gomes, Rafael A.M.; Ribas, Sabir: Integer programming techniques for the nurse rostering problem (2016)
  12. Wang, Zheming; Ong, Chong-Jin: Distributed MPC of constrained linear systems with time-varying terminal sets (2016)
  13. Berthold, Timo; Hendel, Gregor: Shift-and-propagate (2015)
  14. Brito, Samuel Souza; Santos, Haroldo Gambini; Poggi, Marcus: A computational study of conflict graphs and aggressive cut separation in integer programming (2015)
  15. Huang, Kuo-Ling; Mehrotra, Sanjay: An empirical evaluation of a walk-relax-round heuristic for mixed integer convex programs (2015)
  16. Mason, Luke R.; Mak-Hau, Vicky H.; Ernst, Andreas T.: A parallel optimisation approach for the realisation problem in intensity modulated radiotherapy treatment planning (2015)
  17. Omer, Jérémy; Towhidi, Mehdi; Soumis, François: The positive edge pricing rule for the dual simplex (2015)
  18. -: COAP 2013 Best Paper Prize (2014)
  19. Berthold, Timo: RENS. The optimal rounding (2014)
  20. Constantine, P.G.; Phipps, E.T.; Wildey, T.M.: Efficient uncertainty propagation for network multiphysics systems (2014)

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