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 124 articles , 1 standard article )

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  1. Abgrall, Rémi; Öffner, Philipp; Ranocha, Hendrik: Reinterpretation and extension of entropy correction terms for residual distribution and discontinuous Galerkin schemes: application to structure preserving discretization (2022)
  2. Berger, Mathias; Radu, David; Dubois, Antoine; Pandžić, Hrvoje; Dvorkin, Yury; Louveaux, Quentin; Ernst, Damien: Siting renewable power generation assets with combinatorial optimisation (2022)
  3. Bertsimas, Dimitris; Dunn, Jack; Kapelevich, Lea; Zhang, Rebecca: Sparse regression over clusters: SparClur (2022)
  4. Cambier, Adrien; Chardy, Matthieu; Figueiredo, Rosa; Ouorou, Adam; Poss, Michael: Optimizing subscriber migrations for a telecommunication operator in uncertain context (2022)
  5. Fairbrother, Jamie; Turner, Amanda; Wallace, Stein W.: Problem-driven scenario generation: an analytical approach for stochastic programs with tail risk measure (2022)
  6. Générau, François; Oudet, Edouard; Velichkov, Bozhidar: Numerical computation of the cut locus via a variational approximation of the distance function (2022)
  7. Goldberg, Noam; Poss, Michael: Linearized formulations for failure aware barter exchange (2022)
  8. Lundell, Andreas; Kronqvist, Jan: Polyhedral approximation strategies for nonconvex mixed-integer nonlinear programming in SHOT (2022)
  9. Ridler, Samuel; Mason, Andrew J.; Raith, Andrea: A simulation and optimisation package for emergency medical services (2022)
  10. Schettini, Tommaso; Jabali, Ola; Malucelli, Federico: A Benders decomposition algorithm for demand-driven metro scheduling (2022)
  11. Siddig, Murwan; Song, Yongjia: Adaptive partition-based SDDP algorithms for multistage stochastic linear programming with fixed recourse (2022)
  12. Souto, Mario; Garcia, Joaquim D.; Veiga, Álvaro: Exploiting low-rank structure in semidefinite programming by approximate operator splitting (2022)
  13. Tangi Migot; Dominique Orban; Abel Soares Siqueira: DCISolver.jl: A Julia Solver for Nonlinear Optimization using Dynamic Control of Infeasibility (2022) not zbMATH
  14. Wang, Jie; Magron, Victor: Exploiting sparsity in complex polynomial optimization (2022)
  15. Acuña-Zegarra, Manuel Adrian; Díaz-Infante, Saúl; Baca-Carrasco, David; Olmos-Liceaga, Daniel: COVID-19 optimal vaccination policies: a modeling study on efficacy, natural and vaccine-induced immunity responses (2021)
  16. Aliano Filho, Angelo; Melo, Teresa; Pato, Margarida Vaz: A bi-objective mathematical model for integrated planning of sugarcane harvesting and transport operations (2021)
  17. Bertsimas, Dimitris; Cory-Wright, Ryan; Pauphilet, Jean: A unified approach to mixed-integer optimization problems with logical constraints (2021)
  18. Bertsimas, Dimitris; Mundru, Nishanth: Sparse convex regression (2021)
  19. Bertsimas, Dimitris; Pauphilet, Jean; Van Parys, Bart: Sparse classification: a scalable discrete optimization perspective (2021)
  20. Chalumeau, Félix; Coulon, Ilan; Cappart, Quentin; Rousseau, Louis-Martin: SeaPearl: a constraint programming solver guided by reinforcement learning (2021)

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