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 58 articles )

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  1. Escudero, Laureano F.; Garín, María Araceli; Unzueta, Aitziber: Cluster Lagrangean decomposition in multistage stochastic optimization (2016)
  2. Gassmann, Horand; Ma, Jun; Martin, Kipp: Communication protocols for options and results in a distributed optimization environment (2016)
  3. Lejeune, Miguel A.; Margot, François: Solving chance-constrained optimization problems with stochastic quadratic inequalities (2016)
  4. Nemati, Sepehr; Shylo, Oleg V.; Prokopyev, Oleg A.; Schaefer, Andrew J.: The surgical patient routing problem: a central planner approach (2016)
  5. Santos, Haroldo G.; Toffolo, Túlio A.M.; Gomes, Rafael A.M.; Ribas, Sabir: Integer programming techniques for the nurse rostering problem (2016)
  6. Huang, Kuo-Ling; Mehrotra, Sanjay: An empirical evaluation of a walk-relax-round heuristic for mixed integer convex programs (2015)
  7. 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)
  8. Berthold, Timo: RENS. The optimal rounding (2014)
  9. Digne, Julie; Cohen-Steiner, David; Alliez, Pierre; De Goes, Fernando; Desbrun, Mathieu: Feature-preserving surface reconstruction and simplification from defect-laden point sets (2014)
  10. Aldasoro, Unai; Escudero, Laureano F.; Merino, María; Pérez, Gloria: An algorithmic framework for solving large-scale multistage stochastic mixed 0-1 problems with nonsymmetric scenario trees. II: Parallelization (2013)
  11. Balas, Egon; Qualizza, Andrea: Intersection cuts from multiple rows: a disjunctive programming approach (2013)
  12. Belotti, Pietro: Bound reduction using pairs of linear inequalities (2013)
  13. Berthold, Timo: Measuring the impact of primal heuristics (2013)
  14. Escudero, Laureano F.; Araceli Garín, M.; Pérez, Gloria; Unzueta, Aitziber: Scenario cluster decomposition of the Lagrangian dual in two-stage stochastic mixed 0-1 optimization (2013)
  15. Fasano, Giorgio: A global optimization point of view to handle non-standard object packing problems (2013)
  16. Frangioni, Antonio; Gorgone, Enrico: A library for continuous convex separable quadratic knapsack problems (2013)
  17. Huang, Kuo-Ling; Mehrotra, Sanjay: An empirical evaluation of walk-and-round heuristics for mixed integer linear programs (2013)
  18. Lang, Jan Christian; Widjaja, Thomas: OREX-J: Towards a universal software framework for the experimental analysis of optimization algorithms (2013)
  19. Rios, Luis Miguel; Sahinidis, Nikolaos V.: Derivative-free optimization: a review of algorithms and comparison of software implementations (2013)
  20. Aranburu, L.; Escudero, L.F.; Garín, M.A.; Pérez, G.: A so-called cluster Benders decomposition approach for solving two-stage stochastic linear problems (2012)

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