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 57 articles , 1 standard article )

Showing results 21 to 40 of 57.
Sorted by year (citations)
  1. Costa, Giorgio; Kwon, Roy H.: Risk parity portfolio optimization under a Markov regime-switching framework (2019)
  2. Dvorkin, Vladimir; Kazempour, Jalal; Pinson, Pierre: Electricity market equilibrium under information asymmetry (2019)
  3. Francesco Farina, Andrea Camisa, Andrea Testa, Ivano Notarnicola, Giuseppe Notarstefano: DISROPT: a Python Framework for Distributed Optimization (2019) arXiv
  4. Hesaraki, Alireza F.; Dellaert, Nico P.; de Kok, Ton: Generating outpatient chemotherapy appointment templates with balanced flowtime and makespan (2019)
  5. Kaluba, Marek; Nowak, Piotr W.; Ozawa, Narutaka: (\Aut(\mathbbF_5)) has property ((T)) (2019)
  6. Kamiński, Bogumił; Olczak, Tomasz; Prałat, Paweł: Parallel execution of schedules with random dependency graph (2019)
  7. Keys, Kevin L.; Zhou, Hua; Lange, Kenneth: Proximal distance algorithms: theory and practice (2019)
  8. 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)
  9. 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)
  10. Mathieu Besançon: A Julia package for bilevel optimization problems (2019) not zbMATH
  11. Nagarajan, Harsha; Lu, Mowen; Wang, Site; Bent, Russell; Sundar, Kaarthik: An adaptive, multivariate partitioning algorithm for global optimization of nonconvex programs (2019)
  12. Pal, Aritra; Charkhgard, Hadi: FPBH: a feasibility pump based heuristic for multi-objective mixed integer linear programming (2019)
  13. Pessoa, Artur; Sadykov, Ruslan; Uchoa, Eduardo; Vanderbeck, François: A generic exact solver for Vehicle Routing and related problems (2019)
  14. Stathopoulos, Giorgos; Jones, Colin N.: An inertial parallel and asynchronous forward-backward iteration for distributed convex optimization (2019)
  15. Teichgraeber, H., Kuepper, L.,Brandt, A.: TimeSeriesClustering: An extensible framework in Julia (2019) not zbMATH
  16. Wenzel, Simon; Misz, Yannik-Noel; Rahimi-Adli, Keivan; Beisheim, Benedikt; Gesthuisen, Ralf; Engell, Sebastian: An optimization model for site-wide scheduling of coupled production plants with an application to the ammonia network of a petrochemical site (2019)
  17. Yan, Chiwei; Swaroop, Prem; Ball, Michael O.; Barnhart, Cynthia; Vaze, Vikrant: Majority judgment over a convex candidate space (2019)
  18. Ales, Zacharie; Nguyen, Thi Sang; Poss, Michael: Minimizing the weighted sum of completion times under processing time uncertainty (2018)
  19. Alfonso Landeros, Timothy Stutz, Kevin L. Keys, Alexander Alekseyenko, Janet S. Sinsheimer, Kenneth Lange, Mary Sehl: BioSimulator.jl: Stochastic simulation in Julia (2018) arXiv
  20. Benham, G. P.; Hewitt, I. J.; Please, C. P.; Bird, P. A. D.: Optimal control of diffuser shapes for non-uniform flow (2018)