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 56 articles , 1 standard article )

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  1. Allman, Andrew; Zhang, Qi: Dynamic location of modular manufacturing facilities with relocation of individual modules (2020)
  2. Bertsimas, Dimitris; Cory-Wright, Ryan: On polyhedral and second-order cone decompositions of semidefinite optimization problems (2020)
  3. Bonafini, Mauro; Oudet, Édouard: A convex approach to the Gilbert-Steiner problem (2020)
  4. Coey, Chris; Lubin, Miles; Vielma, Juan Pablo: Outer approximation with conic certificates for mixed-integer convex problems (2020)
  5. Goldberg, Noam; Poss, Michael: Maximum probabilistic all-or-nothing paths (2020)
  6. Jagtenberg, C. J.; Mason, A. J.: Improving fairness in ambulance planning by time sharing (2020)
  7. Leclère, Vincent; Carpentier, Pierre; Chancelier, Jean-Philippe; Lenoir, Arnaud; Pacaud, François: Exact converging bounds for stochastic dual dynamic programming via Fenchel duality (2020)
  8. Legat, Benoît; Tabuada, Paulo; Jungers, Raphaël M.: Sum-of-squares methods for controlled invariant sets with applications to model-predictive control (2020)
  9. Lucas Elias Kuepper; Holger Teichgraeber; Adam R. Brandt: CapacityExpansion: A capacity expansion modeling framework in Julia (2020) not zbMATH
  10. Marques, Guillaume; Sadykov, Ruslan; Deschamps, Jean-Christophe; Dupas, Rémy: An improved branch-cut-and-price algorithm for the two-echelon capacitated vehicle routing problem (2020)
  11. Na, Sen; Anitescu, Mihai: Exponential decay in the sensitivity analysis of nonlinear dynamic programming (2020)
  12. Orban, Dominique; Siqueira, Abel Soares: A regularization method for constrained nonlinear least squares (2020)
  13. Pessoa, Artur; Sadykov, Ruslan; Uchoa, Eduardo; Vanderbeck, François: A generic exact solver for vehicle routing and related problems (2020)
  14. Queiroga, Eduardo; Frota, Yuri; Sadykov, Ruslan; Subramanian, Anand; Uchoa, Eduardo; Vidal, Thibaut: On the exact solution of vehicle routing problems with backhauls (2020)
  15. Santiago Badia; Francesc Verdugo: Gridap: An extensible Finite Element toolbox in Julia (2020) not zbMATH
  16. Andersson, Joel A. E.; Gillis, Joris; Horn, Greg; Rawlings, James B.; Diehl, Moritz: CasADi: a software framework for nonlinear optimization and optimal control (2019)
  17. Berk, Lauren; Bertsimas, Dimitris: Certifiably optimal sparse principal component analysis (2019)
  18. Berk, Lauren; Bertsimas, Dimitris; Weinstein, Alexander M.; Yan, Julia: Prescriptive analytics for human resource planning in the professional services industry (2019)
  19. Contardo, Claudio; Iori, Manuel; Kramer, Raphael: A scalable exact algorithm for the vertex (p)-center problem (2019)
  20. Costa, Giorgio; Kwon, Roy H.: Risk parity portfolio optimization under a Markov regime-switching framework (2019)

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