JuMP: A Modeling Language for Mathematical Optimization. JuMP is an open-source modeling language that allows users to express a wide range of optimization problems (linear, mixed-integer, quadratic, conic-quadratic, semidefinite, and nonlinear) in a high-level, algebraic syntax. JuMP takes advantage of advanced features of the Julia programming language to offer unique functionality while achieving performance on par with commercial modeling tools for standard tasks. In this work we will provide benchmarks, present the novel aspects of the implementation, and discuss how JuMP can be extended to new problem classes and composed with state-of-the-art tools for visualization and interactivity.

References in zbMATH (referenced in 97 articles , 1 standard article )

Showing results 1 to 20 of 97.
Sorted by year (citations)

1 2 3 4 5 next

  1. Bertsimas, Dimitris; Mundru, Nishanth: Sparse convex regression (2021)
  2. Dandurand, Brian C.; Kim, Kibaek; Leyffer, Sven: A bilevel approach for identifying the worst contingencies for nonconvex alternating current power systems (2021)
  3. Davarnia, Danial; van Hoeve, Willem-Jan: Outer approximation for integer nonlinear programs via decision diagrams (2021)
  4. de OliveiraResende, Larissa; Valladão, Davi; Bezerra, Bernardo Vieira; Cyrillo, Yasmin Monteiro: Assessing the value of natural gas underground storage in the Brazilian system via stochastic dual dynamic programming (2021)
  5. Dowson, Oscar; Kapelevich, Lea: SDDP.jl: a Julia package for stochastic dual dynamic programming (2021)
  6. Francesco Ceccon, Ruth Misener: Solving the pooling problem at scale with extensible solver GALINI (2021) arXiv
  7. García, Hernán; Hernández, Camilo; Junca, Mauricio; Velasco, Mauricio: Approximate super-resolution of positive measures in all dimensions (2021)
  8. Garstka, Michael; Cannon, Mark; Goulart, Paul: COSMO: a conic operator splitting method for convex conic problems (2021)
  9. Goldberg, Noam; Rebennack, Steffen; Kim, Youngdae; Krasko, Vitaliy; Leyffer, Sven: MINLP formulations for continuous piecewise linear function fitting (2021)
  10. Haeser, Gabriel; Hinder, Oliver; Ye, Yinyu: On the behavior of Lagrange multipliers in convex and nonconvex infeasible interior point methods (2021)
  11. Kaluba, Marek; Kielak, Dawid; Nowak, Piotr: On property (T) for (\mathrmAut(F_n)) and (\mathrmSL_n(\mathbbZ)) (2021)
  12. Karimi, Hadi; Ekşioğlu, Sandra D.; Carbajales-Dale, Michael: A biobjective chance constrained optimization model to evaluate the economic and environmental impacts of biopower supply chains (2021)
  13. Lasserre, Jean Bernard; Magron, Victor; Marx, Swann; Zahm, Olivier: Minimizing rational functions: a hierarchy of approximations via pushforward measures (2021)
  14. Manousakis, Eleftherios; Repoussis, Panagiotis; Zachariadis, Emmanouil; Tarantilis, Christos: Improved branch-and-cut for the inventory routing problem based on a two-commodity flow formulation (2021)
  15. Mathieu Besancon, Alejandro Carderera, Sebastian Pokutta: FrankWolfe.jl: a high-performance and flexible toolbox for Frank-Wolfe algorithms and Conditional Gradients (2021) arXiv
  16. Mixon, Dustin G.; Parshall, Hans: Globally optimizing small codes in real projective spaces (2021)
  17. O’Donoghue, Brendan: Operator splitting for a homogeneous embedding of the linear complementarity problem (2021)
  18. Pessoa, Artur; Sadykov, Ruslan; Uchoa, Eduardo: Solving bin packing problems using VRPSolver models (2021)
  19. Sundar, Kaarthik; Nagarajan, Harsha; Linderoth, Jeff; Wang, Site; Bent, Russell: Piecewise polyhedral formulations for a multilinear term (2021)
  20. Tarhini, Hussein; Maddah, Bacel; Hamzeh, Farook: The traveling salesman puts-on a hard hat -- tower crane scheduling in construction projects (2021)

1 2 3 4 5 next