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 35 articles , 1 standard article )

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  1. Jagtenberg, C. J.; Mason, A. J.: Improving fairness in ambulance planning by time sharing (2020)
  2. 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)
  3. Andersson, Joel A. E.; Gillis, Joris; Horn, Greg; Rawlings, James B.; Diehl, Moritz: CasADi: a software framework for nonlinear optimization and optimal control (2019)
  4. Berk, Lauren; Bertsimas, Dimitris; Weinstein, Alexander M.; Yan, Julia: Prescriptive analytics for human resource planning in the professional services industry (2019)
  5. Contardo, Claudio; Iori, Manuel; Kramer, Raphael: A scalable exact algorithm for the vertex (p)-center problem (2019)
  6. Costa, Giorgio; Kwon, Roy H.: Risk parity portfolio optimization under a Markov regime-switching framework (2019)
  7. Francesco Farina, Andrea Camisa, Andrea Testa, Ivano Notarnicola, Giuseppe Notarstefano: DISROPT: a Python Framework for Distributed Optimization (2019) arXiv
  8. Hesaraki, Alireza F.; Dellaert, Nico P.; de Kok, Ton: Generating outpatient chemotherapy appointment templates with balanced flowtime and makespan (2019)
  9. Kamiński, Bogumił; Olczak, Tomasz; Prałat, Paweł: Parallel execution of schedules with random dependency graph (2019)
  10. Kronqvist, Jan; Bernal, David E.; Lundell, Andreas; Grossmann, Ignacio E.: A review and comparison of solvers for convex MINLP (2019)
  11. Lee, Do Yeon; Fukasawa, Ricardo; Ricardez-Sandoval, Luis: Bi-objective short-term scheduling in a rolling horizon framework: a priori approaches with alternative operational objectives (2019)
  12. Li, Can; Grossmann, Ignacio E.: A finite (\epsilon)-convergence algorithm for two-stage stochastic convex nonlinear programs with mixed-binary first and second-stage variables (2019)
  13. Mathieu Besançon: A Julia package for bilevel optimization problems (2019) not zbMATH
  14. Nagarajan, Harsha; Lu, Mowen; Wang, Site; Bent, Russell; Sundar, Kaarthik: An adaptive, multivariate partitioning algorithm for global optimization of nonconvex programs (2019)
  15. Pal, Aritra; Charkhgard, Hadi: FPBH: a feasibility pump based heuristic for multi-objective mixed integer linear programming (2019)
  16. Stathopoulos, Giorgos; Jones, Colin N.: An inertial parallel and asynchronous forward-backward iteration for distributed convex optimization (2019)
  17. Ales, Zacharie; Nguyen, Thi Sang; Poss, Michael: Minimizing the weighted sum of completion times under processing time uncertainty (2018)
  18. Alfonso Landeros, Timothy Stutz, Kevin L. Keys, Alexander Alekseyenko, Janet S. Sinsheimer, Kenneth Lange, Mary Sehl: BioSimulator.jl: Stochastic simulation in Julia (2018) arXiv
  19. Benham, G. P.; Hewitt, I. J.; Please, C. P.; Bird, P. A. D.: Optimal control of diffuser shapes for non-uniform flow (2018)
  20. Bonafini, M.: Convex relaxation and variational approximation of the Steiner problem: theory and numerics (2018)

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