AMPL is a comprehensive and powerful algebraic modeling language for linear and nonlinear optimization problems, in discrete or continuous variables. Developed at Bell Laboratories, AMPL lets you use common notation and familiar concepts to formulate optimization models and examine solutions, while the computer manages communication with an appropriate solver. AMPL’s flexibility and convenience render it ideal for rapid prototyping and model development, while its speed and control options make it an especially efficient choice for repeated production runs.

References in zbMATH (referenced in 607 articles , 2 standard articles )

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  1. Maskooki, Alaleh; Deb, Kalyanmoy; Kallio, Markku: A customized genetic algorithm for bi-objective routing in a dynamic network (2022)
  2. Aftalion, Amandine; Trélat, Emmanuel: Pace and motor control optimization for a runner (2021)
  3. Burachik, Regina S.; Kalloniatis, Alexander C.; Kaya, C. Yalçın: Sparse network optimization for synchronization (2021)
  4. Cerulli, Martina; D’Ambrosio, Claudia; Liberti, Leo; Pelegrín, Mercedes: Detecting and solving aircraft conflicts using bilevel programming (2021)
  5. Chen, X.; Toint, Ph. L.: High-order evaluation complexity for convexly-constrained optimization with non-Lipschitzian group sparsity terms (2021)
  6. Dias, Gustavo; Liberti, Leo: Exploiting symmetries in mathematical programming via orbital independence (2021)
  7. Ding Ma, Dominique Orban, Michael A. Saunders: A Julia implementation of Algorithm NCL for constrained optimization (2021) arXiv
  8. Di Pillo, Gianni; Fabiano, Marcello; Lucidi, Stefano; Roma, Massimo: Cruise itineraries optimal scheduling (2021)
  9. Elloumi, Sourour; Hudry, Olivier; Marie, Estel; Martin, Agathe; Plateau, Agnès; Rovedakis, Stéphane: Optimization of wireless sensor networks deployment with coverage and connectivity constraints (2021)
  10. Francesco Ceccon, Ruth Misener: Solving the pooling problem at scale with extensible solver GALINI (2021) arXiv
  11. Lohmann, Timo; Bussieck, Michael R.; Westermann, Lutz; Rebennack, Steffen: High-performance prototyping of decomposition methods in GAMS (2021)
  12. Mahajan, Ashutosh; Leyffer, Sven; Linderoth, Jeff; Luedtke, James; Munson, Todd: Minotaur: a mixed-integer nonlinear optimization toolkit (2021)
  13. Mtonga, Kambombo; Twahirwa, Evariste; Kumaran, Santhi: Modelling classroom space allocation at University of Rwanda -- a linear programming approach (2021)
  14. Pawlak, Tomasz P.; Litwiniuk, Bartosz: Ellipsoidal one-class constraint acquisition for quadratically constrained programming (2021)
  15. Schoen, Fabio; Tigli, Luca: Efficient large scale global optimization through clustering-based population methods (2021)
  16. Wolsey, Laurence A.: Integer programming (2021)
  17. Belle, Vaishak; De Raedt, Luc: Semiring programming: a semantic framework for generalized sum product problems (2020)
  18. Bienstock, Dan; Escobar, Mauro; Gentile, Claudio; Liberti, Leo: Mathematical programming formulations for the alternating current optimal power flow problem (2020)
  19. Casanellas, Glòria; Castro, Jordi: Using interior point solvers for optimizing progressive lens models with spherical coordinates (2020)
  20. Chepoi, Victor; Knauer, Kolja; Philibert, Manon: Two-dimensional partial cubes (2020)

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