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 144 articles )

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  1. 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)
  2. Dumnicki, M.; Harrer, D.; Szpond, J.: On absolute linear Harbourne constants (2018)
  3. Gurski, Frank; Rethmann, Jochen: Distributed solving of mixed-integer programs with GLPK and Thrift (2018)
  4. Lancia, Giuseppe; Serafini, Paolo: Compact extended linear programming models (2018)
  5. Megahed, Aly; Goetschalckx, Marc: Tactical supply chain planning under uncertainty with an application in the wind turbines industry (2018)
  6. Abdelaati Daouia and Thibault Laurent and Hohsuk Noh: npbr: A Package for Nonparametric Boundary Regression in R (2017)
  7. Bertsimas, Dimitris; King, Angela: Logistic regression: from art to science (2017)
  8. Bouzid, Mouaouia Cherif; Aït Haddadene, Hacene; Salhi, Said: An integration of Lagrangian split and VNS: the case of the capacitated vehicle routing problem (2017)
  9. Cervantes, Víctor H.; Dzhafarov, Ehtibar N.: Advanced analysis of quantum contextuality in a psychophysical double-detection experiment (2017)
  10. Deb, Kalyanmoy; Myburgh, Christie: A population-based fast algorithm for a billion-dimensional resource allocation problem with integer variables (2017)
  11. Gerber, Samuel; Maggioni, Mauro: Multiscale strategies for computing optimal transport (2017)
  12. 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)
  13. Rudloff, Birgit; Ulus, Firdevs; Vanderbei, Robert: A parametric simplex algorithm for linear vector optimization problems (2017)
  14. Toth, Csaba D. (ed.); Goodman, Jacob E. (ed.); O’Rourke, Joseph (ed.): Handbook of discrete and computational geometry (2017)
  15. Whidden, Chris; Matsen, Frederick A. IV: Ricci-Ollivier curvature of the rooted phylogenetic subtree-prune-regraft graph (2017)
  16. Ansótegui, Carlos; Bofill, Miquel; Manyà, Felip; Villaret, Mateu: Automated theorem provers for multiple-valued logics with satisfiability modulo theory solvers (2016)
  17. Birgin, E. G.; Martínez, J. M.: On the application of an augmented Lagrangian algorithm to some portfolio problems (2016)
  18. Craciunas, Silviu S.; Oliver, Ramon Serna: Combined task- and network-level scheduling for distributed time-triggered systems (2016)
  19. Delanoue, Nicolas; Lhommeau, Mehdi; Lucidarme, Philippe: Numerical enclosures of the optimal cost of the Kantorovitch’s mass transportation problem (2016)
  20. Ferrer Fioriti, Luis María; Hashemi, Vahid; Hermanns, Holger; Turrini, Andrea: Deciding probabilistic automata weak bisimulation: theory and practice (2016)

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