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 121 articles )

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  1. Gurski, Frank; Rethmann, Jochen: Distributed solving of mixed-integer programs with GLPK and Thrift (2018)
  2. Cervantes, Víctor H.; Dzhafarov, Ehtibar N.: Advanced analysis of quantum contextuality in a psychophysical double-detection experiment (2017)
  3. 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)
  4. Rudloff, Birgit; Ulus, Firdevs; Vanderbei, Robert: A parametric simplex algorithm for linear vector optimization problems (2017)
  5. Toth, Csaba D. (ed.); Goodman, Jacob E. (ed.); O’Rourke, Joseph (ed.): Handbook of discrete and computational geometry (2017)
  6. Whidden, Chris; Matsen, Frederick A.IV: Ricci-ollivier curvature of the rooted phylogenetic subtree-prune-regraft graph (2017)
  7. Birgin, E.G.; Martínez, J.M.: On the application of an augmented Lagrangian algorithm to some portfolio problems (2016)
  8. Craciunas, Silviu S.; Oliver, Ramon Serna: Combined task- and network-level scheduling for distributed time-triggered systems (2016)
  9. Delanoue, Nicolas; Lhommeau, Mehdi; Lucidarme, Philippe: Numerical enclosures of the optimal cost of the Kantorovitch’s mass transportation problem (2016)
  10. Ferrer Fioriti, Luis María; Hashemi, Vahid; Hermanns, Holger; Turrini, Andrea: Deciding probabilistic automata weak bisimulation: theory and practice (2016)
  11. Gassmann, Horand; Ma, Jun; Martin, Kipp: Communication protocols for options and results in a distributed optimization environment (2016)
  12. Johnston, Matthew D.: A linear programming approach to dynamical equivalence, linear conjugacy, and the deficiency one theorem (2016)
  13. Johnston, Matthew D.; Pantea, Casian; Donnell, Pete: A computational approach to persistence, permanence, and endotacticity of biochemical reaction systems (2016)
  14. Kersbergen, Bart; Rudan, János; van den Boom, Ton; De Schutter, Bart: Towards railway traffic management using switching max-plus-linear systems, structure analysis and rescheduling (2016)
  15. Tozoni, Davi C.; De Rezende, Pedro J.; De Souza, Cid C.: Algorithm 966: a practical iterative algorithm for the art gallery problem using integer linear programming (2016)
  16. Bemporad, Alberto: A multiparametric quadratic programming algorithm with polyhedral computations based on nonnegative least squares (2015)
  17. Booker, Andrew R.; Hiary, Ghaith A.; Keating, Jon P.: Detecting squarefree numbers (2015)
  18. Bouzid, Mouaouia Cherif: Splitting a giant tour using integer linear programming (2015)
  19. Calegari, Danny; Walker, Alden: Surface subgroups from linear programming (2015)
  20. Chatterjee, Krishnendu; Henzinger, Thomas A.; Jobstmann, Barbara; Singh, Rohit: Measuring and synthesizing systems in probabilistic environments (2015)

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