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 107 articles )

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  1. 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)
  2. Rudloff, Birgit; Ulus, Firdevs; Vanderbei, Robert: A parametric simplex algorithm for linear vector optimization problems (2017)
  3. Birgin, E.G.; Martínez, J.M.: On the application of an augmented Lagrangian algorithm to some portfolio problems (2016)
  4. Craciunas, Silviu S.; Oliver, Ramon Serna: Combined task- and network-level scheduling for distributed time-triggered systems (2016)
  5. Delanoue, Nicolas; Lhommeau, Mehdi; Lucidarme, Philippe: Numerical enclosures of the optimal cost of the Kantorovitch’s mass transportation problem (2016)
  6. Ferrer Fioriti, Luis María; Hashemi, Vahid; Hermanns, Holger; Turrini, Andrea: Deciding probabilistic automata weak bisimulation: theory and practice (2016)
  7. Gassmann, Horand; Ma, Jun; Martin, Kipp: Communication protocols for options and results in a distributed optimization environment (2016)
  8. Johnston, Matthew D.: A linear programming approach to dynamical equivalence, linear conjugacy, and the deficiency one theorem (2016)
  9. Johnston, Matthew D.; Pantea, Casian; Donnell, Pete: A computational approach to persistence, permanence, and endotacticity of biochemical reaction systems (2016)
  10. 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)
  11. 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)
  12. Bouzid, Mouaouia Cherif: Splitting a giant tour using integer linear programming (2015)
  13. El Otmani, S.; Rhin, G.; Sac-Épée, J.-M.: A salem number with degree 34 and trace -3 (2015)
  14. Kirst, Peter; Stein, Oliver; Steuermann, Paul: Deterministic upper bounds for spatial branch-and-bound methods in global minimization with nonconvex constraints (2015)
  15. Kubica, Bartłomiej Jacek: Presentation of a highly tuned multithreaded interval solver for underdetermined and well-determined nonlinear systems (2015)
  16. Kuno, Takahito; Ishihama, Tomohiro: A convergent conical algorithm with $\omega $-bisection for concave minimization (2015)
  17. Lipták, György; Szederkényi, Gábor; Hangos, Katalin M.: Computing zero deficiency realizations of kinetic systems (2015)
  18. Romauch, Martin; Klemmt, Andreas: Product mix optimization for a semiconductor fab: modeling approaches and decomposition techniques (2015)
  19. Giro, Sergio: Optimal schedulers vs optimal bases: an approach for efficient exact solving of Markov decision processes (2014)
  20. Gondzio, Jacek; Gruca, Jacek A.; Hall, J.A.Julian; Laskowski, Wiesław; Żukowski, Marek: Solving large-scale optimization problems related to Bell’s theorem (2014)

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