GLPK

The GLPK (GNU Linear Programming Kit) package is intended for solving large-scale linear programming (LP), mixed integer programming (MIP), and other related problems. It is a set of routines written in ANSI C and organized in the form of a callable library. GLPK supports the GNU MathProg modeling language, which is a subset of the AMPL language. The GLPK package includes the following main components: primal and dual simplex methods, primal-dual interior-point method, branch-and-cut method, translator for GNU MathProg, application program interface (API), stand-alone LP/MIP solver


References in zbMATH (referenced in 181 articles )

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  1. Basile, Francesco; Boccia, Maurizio; De Tommasi, Gianmaria; Motta, Carlo; Sterle, Claudio: An optimization-based approach to assess non-interference in labeled and bounded Petri net systems (2022)
  2. Csirmaz, Laszlo: Inner approximation algorithm for solving linear multiobjective optimization problems (2021)
  3. Dahmani, Isma; Hifi, Mhand: A modified descent method-based heuristic for binary quadratic knapsack problems with conflict graphs (2021)
  4. Gavrilyuk, Alexander L.; Vidali, Janoš; Williford, Jason S.: On few-class (Q)-polynomial association schemes: feasible parameters and nonexistence results (2021)
  5. Jesus, Alexandre D.; Paquete, Luís; Liefooghe, Arnaud: A model of anytime algorithm performance for bi-objective optimization (2021)
  6. Mazyavkina, Nina; Sviridov, Sergey; Ivanov, Sergei; Burnaev, Evgeny: Reinforcement learning for combinatorial optimization: a survey (2021)
  7. Moehle, Nicholas; Kochenderfer, Mykel J.; Boyd, Stephen; Ang, Andrew: Tax-aware portfolio construction via convex optimization (2021)
  8. Schwendinger, Florian; Grün, Bettina; Hornik, Kurt: A comparison of optimization solvers for log binomial regression including conic programming (2021)
  9. Stetsyuk, Petro; Fischer, Andreas; Pichugina, Oksana: A penalty approach to linear programs with many two-sided constraints (2021)
  10. Tanneau, Mathieu; Anjos, Miguel F.; Lodi, Andrea: Design and implementation of a modular interior-point solver for linear optimization (2021)
  11. Chen, Qian Matteo; Finzi, Alberto; Mancini, Toni; Melatti, Igor; Tronci, Enrico: MILP, pseudo-Boolean, and OMT solvers for optimal fault-tolerant placements of relay nodes in mission critical wireless networks (2020)
  12. Costalonga, João Paulo: Toroidal boards and code covering (2020)
  13. Degue, Kwassi H.; Le Ny, Jerome: Estimation and outbreak detection with interval observers for uncertain discrete-time SEIR epidemic models (2020)
  14. Delanoue, Nicolas; Lhommeau, Mehdi; Lagrange, Sébastien: Nonlinear optimal control: a numerical scheme based on occupation measures and interval analysis (2020)
  15. García Soto, Miriam; Prabhakar, Pavithra: Abstraction based verification of stability of polyhedral switched systems (2020)
  16. Héger, Tamás; Szilárd, Péter; Takáts, Marcella: The metric dimension of the incidence graphs of projective planes of small order (2020)
  17. Kloetzer, Marius; Mahulea, Cristian: Path planning for robotic teams based on LTL specifications and Petri net models (2020)
  18. Bojarski, Jacek; Kisielewicz, Andrzej Piotr; Przesławski, Krzysztof: Nearly neighbourly families of standard boxes (2019)
  19. Ferrier, R.; Kadri, M. L.; Gosselet, P.: Planar crack identification in 3D linear elasticity by the reciprocity gap method (2019)
  20. Francesco Farina, Andrea Camisa, Andrea Testa, Ivano Notarnicola, Giuseppe Notarstefano: DISROPT: a Python Framework for Distributed Optimization (2019) arXiv

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