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

Keywords for this software

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References in zbMATH (referenced in 172 articles )

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  1. Dahmani, Isma; Hifi, Mhand: A modified descent method-based heuristic for binary quadratic knapsack problems with conflict graphs (2021)
  2. Jesus, Alexandre D.; Paquete, Luís; Liefooghe, Arnaud: A model of anytime algorithm performance for bi-objective optimization (2021)
  3. 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)
  4. Costalonga, João Paulo: Toroidal boards and code covering (2020)
  5. Degue, Kwassi H.; Le Ny, Jerome: Estimation and outbreak detection with interval observers for uncertain discrete-time SEIR epidemic models (2020)
  6. Delanoue, Nicolas; Lhommeau, Mehdi; Lagrange, Sébastien: Nonlinear optimal control: a numerical scheme based on occupation measures and interval analysis (2020)
  7. García Soto, Miriam; Prabhakar, Pavithra: Abstraction based verification of stability of polyhedral switched systems (2020)
  8. Héger, Tamás; Szilárd, Péter; Takáts, Marcella: The metric dimension of the incidence graphs of projective planes of small order (2020)
  9. Kloetzer, Marius; Mahulea, Cristian: Path planning for robotic teams based on LTL specifications and Petri net models (2020)
  10. Bojarski, Jacek; Kisielewicz, Andrzej Piotr; Przesławski, Krzysztof: Nearly neighbourly families of standard boxes (2019)
  11. Ferrier, R.; Kadri, M. L.; Gosselet, P.: Planar crack identification in 3D linear elasticity by the reciprocity gap method (2019)
  12. Francesco Farina, Andrea Camisa, Andrea Testa, Ivano Notarnicola, Giuseppe Notarstefano: DISROPT: a Python Framework for Distributed Optimization (2019) arXiv
  13. Kirst, Peter; Stein, Oliver: Global optimization of generalized semi-infinite programs using disjunctive programming (2019)
  14. Métivier, Ludovic; Brossier, R.; Mérigot, Q.; Oudet, E.: A graph space optimal transport distance as a generalization of (L^p) distances: application to a seismic imaging inverse problem (2019)
  15. Pal, Aritra; Charkhgard, Hadi: FPBH: a feasibility pump based heuristic for multi-objective mixed integer linear programming (2019)
  16. Vidali, Janoš: Computing distance-regular graph and association scheme parameters in \textttSageMathwith \textttsage-\textttdrg (2019)
  17. Wenzel, Simon; Misz, Yannik-Noel; Rahimi-Adli, Keivan; Beisheim, Benedikt; Gesthuisen, Ralf; Engell, Sebastian: An optimization model for site-wide scheduling of coupled production plants with an application to the ammonia network of a petrochemical site (2019)
  18. Altmanová, Katerina; Knop, Dusan; Koutecký, Martin: Evaluating and tuning (n)-fold integer programming (2018)
  19. Convolbo, Moïse W.; Chou, Jerry; Hsu, Ching-Hsien; Chung, Yeh Ching: GEODIS: towards the optimization of data locality-aware job scheduling in geo-distributed data centers (2018)
  20. Desdouits, Chloé; Alamir, Mazen; Giroudeau, Rodolphe; Le Pape, Claude: A method for the energy optimization of a multisource elevator (2018)

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