AMPL

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 594 articles , 2 standard articles )

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  1. Chen, X.; Toint, Ph. L.: High-order evaluation complexity for convexly-constrained optimization with non-Lipschitzian group sparsity terms (2021)
  2. Dias, Gustavo; Liberti, Leo: Exploiting symmetries in mathematical programming via orbital independence (2021)
  3. Ding Ma, Dominique Orban, Michael A. Saunders: A Julia implementation of Algorithm NCL for constrained optimization (2021) arXiv
  4. Elloumi, Sourour; Hudry, Olivier; Marie, Estel; Martin, Agathe; Plateau, Agnès; Rovedakis, Stéphane: Optimization of wireless sensor networks deployment with coverage and connectivity constraints (2021)
  5. Wolsey, Laurence A.: Integer programming (2021)
  6. Belle, Vaishak; De Raedt, Luc: Semiring programming: a semantic framework for generalized sum product problems (2020)
  7. Bienstock, Dan; Escobar, Mauro; Gentile, Claudio; Liberti, Leo: Mathematical programming formulations for the alternating current optimal power flow problem (2020)
  8. Casanellas, Glòria; Castro, Jordi: Using interior point solvers for optimizing progressive lens models with spherical coordinates (2020)
  9. Chepoi, Victor; Knauer, Kolja; Philibert, Manon: Two-dimensional partial cubes (2020)
  10. Colapinto, Cinzia; Jayaraman, Raja; La Torre, Davide: Goal programming models for managerial strategic decision making (2020)
  11. Degue, Kwassi H.; Le Ny, Jerome: Estimation and outbreak detection with interval observers for uncertain discrete-time SEIR epidemic models (2020)
  12. Dempe, S.; Khamisov, O.; Kochetov, Yu.: A special three-level optimization problem (2020)
  13. Després, Bruno; Trélat, Emmanuel: Two-sided space-time (L^1) polynomial approximation of hypographs within polynomial optimal control (2020)
  14. Francesco Biscani; Dario Izzo: A parallel global multiobjective framework for optimization: pagmo (2020) not zbMATH
  15. Ghosh, Debdas; Sharma, Akshay; Shukla, K. K.; Kumar, Amar; Manchanda, Kartik: Globalized robust Markov perfect equilibrium for discounted stochastic games and its application on intrusion detection in wireless sensor networks. I. Theory (2020)
  16. Hamilton, William T.; Husted, Mark A.; Newman, Alexandra M.; Braun, Robert J.; Wagner, Michael J.: Dispatch optimization of concentrating solar power with utility-scale photovoltaics (2020)
  17. King, Marvin L.; Galbreath, David R.; Newman, Alexandra M.; Hering, Amanda S.: Combining regression and mixed-integer programming to model counterinsurgency (2020)
  18. Koné, Mamadou; Ndiaye, Babacar Mbaye; Seck, Diaraf: Optimal mass transport for activities location problem (2020)
  19. Magiera, Marek: Method of rescheduling for hybrid production lines with intermediate buffers (2020)
  20. Mazari, Idriss; Nadin, Grégoire; Privat, Yannick: Optimal location of resources maximizing the total population size in logistic models (2020)

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