MINTO is a software system that solves mixed-integer linear programs by a branch-and-bound algorithm with linear programming relaxations. It also provides automatic constraint classification, preprocessing, primal heuristics and constraint generation. Moreover, the user can enrich the basic algorithm by providing a variety of specialized application routines that can customize MINTO to achieve maximum efficiency for a problem class. To be as effective and efficient as possible when used as a general purpose mixed-integer optimizer, MINTO attempts to: improve the formulation by preprocessing and probing; construct feasible solutions generate strong valid inequalities perform variable fixing based on reduced prices control the size of the linear programs by managing active constraints. To be as flexible and powerful as possible when used to build a special purpose mixed-integer optimizer, MINTO provides various mechanisms for incorporating problem specific knowledge.

References in zbMATH (referenced in 132 articles )

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  1. Zhang, Yi; Sahinidis, Nikolaos V.; Nohra, Carlos; Rong, Gang: Optimality-based domain reduction for inequality-constrained NLP and MINLP problems (2020)
  2. Kılınç, Mustafa R.; Linderoth, Jeff; Luedtke, James: Lift-and-project cuts for convex mixed integer nonlinear programs (2017)
  3. Puranik, Yash; Sahinidis, Nikolaos V.: Domain reduction techniques for global NLP and MINLP optimization (2017)
  4. Eckstein, Jonathan; Hart, William E.; Phillips, Cynthia A.: PEBBL: an object-oriented framework for scalable parallel branch and bound (2015)
  5. Oliveira, José A.; Ferreira, João; Dias, Luis; Figueiredo, Manuel; Pereira, Guilherme: The non-emergency patient transport modelled as a team orienteering problem (2015)
  6. Kılınç, Mustafa; Linderoth, Jeff; Luedtke, James; Miller, Andrew: Strong-branching inequalities for convex mixed integer nonlinear programs (2014)
  7. Beheshti, Zahra; Shamsuddin, Siti Mariyam; Yuhaniz, Siti Sophiayati: Binary accelerated particle swarm algorithm (BAPSA) for discrete optimization problems (2013)
  8. D’ambrosio, Claudia; Lodi, Andrea: Mixed integer nonlinear programming tools: an updated practical overview (2013)
  9. Pinheiro, Placido Rogerio; Oliveira, Paulo Roberto: A hybrid approach of bundle and Benders applied large mixed linear integer problem (2013)
  10. Bixby, Robert E.: A brief history of linear and mixed-integer programming computation (2012)
  11. Bonami, Pierre; Kilinç, Mustafa; Linderoth, Jeff: Algorithms and software for convex mixed integer nonlinear programs (2012)
  12. Hu, Jing; Mitchell, John E.; Pang, Jong-Shi: An LPCC approach to nonconvex quadratic programs (2012)
  13. Altın, Ayşegül; Yaman, Hande; Pınar, Mustafa Ç.: The robust network loading problem under hose demand uncertainty: formulation, polyhedral analysis, and computations (2011)
  14. D’Ambrosio, Claudia; Lodi, Andrea: Mixed integer nonlinear programming tools: a practical overview (2011)
  15. Lin, Geng; Zhu, Wenxing; Ali, M. M.: An exact algorithm for the 0-1 linear knapsack problem with a single continuous variable (2011)
  16. Ostrowski, James; Linderoth, Jeff; Rossi, Fabrizio; Smriglio, Stefano: Orbital branching (2011)
  17. Puchinger, Jakob; Stuckey, Peter J.; Wallace, Mark G.; Brand, Sebastian: Dantzig-Wolfe decomposition and branch-and-price solving in G12 (2011)
  18. Vanderbeck, François: Branching in branch-and-price: A generic scheme (2011)
  19. Abhishek, Kumar; Leyffer, Sven; Linderoth, Jeff: FilMINT: an outer approximation-based solver for convex mixed-integer nonlinear programs (2010)
  20. Abhishek, Kumar; Leyffer, Sven; Linderoth, Jeffrey T.: Modeling without categorical variables: a mixed-integer nonlinear program for the optimization of thermal insulation systems (2010)

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