JuMP

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 41 to 57 of 57.
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  1. Bonafini, M.: Convex relaxation and variational approximation of the Steiner problem: theory and numerics (2018)
  2. Carpentier, P.; Chancelier, J. -Ph.; Leclère, V.; Pacaud, F.: Stochastic decomposition applied to large-scale hydro valleys management (2018)
  3. Ficker, Annette M. C.; Spieksma, Frits C. R.; Woeginger, Gerhard J.: Robust balanced optimization (2018)
  4. Jordan Jalving, Yankai Cao, Victor M. Zavala: Graph-Based Modeling and Simulation of Complex Systems (2018) arXiv
  5. Kaluba, Marek; Nowak, Piotr W.: Certifying numerical estimates of spectral gaps (2018)
  6. Lubin, Miles; Yamangil, Emre; Bent, Russell; Vielma, Juan Pablo: Polyhedral approximation in mixed-integer convex optimization (2018)
  7. Mustonen, Lauri; Gao, Xiangxi; Santana, Asteroide; Mitchell, Rebecca; Vigfusson, Ymir; Ruthotto, Lars: A Bayesian framework for molecular strain identification from mixed diagnostic samples (2018)
  8. Petra, C. G.; Qiang, F.; Lubin, M.; Huchette, J.: On efficient Hessian computation using the edge pushing algorithm in Julia (2018)
  9. Philpott, A. B.; de Matos, V. L.; Kapelevich, L.: Distributionally robust SDDP (2018)
  10. P. K. Mogensen; A. N. Riseth: Optim: A mathematical optimization package for Julia (2018) not zbMATH
  11. Youngseok Kim, Peter Carbonetto, Matthew Stephens, Mihai Anitescu: A Fast Algorithm for Maximum Likelihood Estimation of Mixture Proportions Using Sequential Quadratic Programming (2018) arXiv
  12. Dunning, Iain; Huchette, Joey; Lubin, Miles: JuMP: a modeling language for mathematical optimization (2017)
  13. Dussault, Jean-Pierre: A note on robust descent in differentiable optimization (2017)
  14. Dvijotham, Krishnamurthy; Chertkov, Michael; van Hentenryck, Pascal; Vuffray, Marc; Misra, Sidhant: Graphical models for optimal power flow (2017)
  15. Vielma, Juan Pablo; Dunning, Iain; Huchette, Joey; Lubin, Miles: Extended formulations in mixed integer conic quadratic programming (2017)
  16. Jarrett Revels, Miles Lubin, Theodore Papamarkou: Forward-Mode Automatic Differentiation in Julia (2016) arXiv
  17. Miles Lubin, Emre Yamangil, Russell Bent, Juan Pablo Vielma: Polyhedral approximation in mixed-integer convex optimization (2016) arXiv