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 574 articles , 2 standard articles )

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  1. Colapinto, Cinzia; Jayaraman, Raja; La Torre, Davide: Goal programming models for managerial strategic decision making (2020)
  2. Dempe, S.; Khamisov, O.; Kochetov, Yu.: A special three-level optimization problem (2020)
  3. 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)
  4. 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)
  5. Mazari, Idriss; Nadin, Grégoire; Privat, Yannick: Optimal location of resources maximizing the total population size in logistic models (2020)
  6. Vanderbei, Robert J.: Linear programming. Foundations and extensions (2020)
  7. Almeida, Luis; Privat, Yannick; Strugarek, Martin; Vauchelet, Nicolas: Optimal releases for population replacement strategies: application to Wolbachia (2019)
  8. Andersson, Joel A. E.; Gillis, Joris; Horn, Greg; Rawlings, James B.; Diehl, Moritz: CasADi: a software framework for nonlinear optimization and optimal control (2019)
  9. Baharev, Ali; Neumaier, Arnold; Schichl, Hermann: A manifold-based approach to sparse global constraint satisfaction problems (2019)
  10. Ballard, Grey; Ikenmeyer, Christian; Landsberg, J. M.; Ryder, Nick: The geometry of rank decompositions of matrix multiplication. II: (3 \times3) matrices (2019)
  11. Boggs, Paul T.; Byrd, Richard H.: Adaptive, limited-memory BFGS algorithms for unconstrained optimization (2019)
  12. Cay, Pelin; Mancilla, Camilo; Storer, Robert H.; Zuluaga, Luis F.: Operational decisions for multi-period industrial gas pipeline networks under uncertainty (2019)
  13. Chen, Xiaojun; Toint, Ph. L.; Wang, H.: Complexity of partially separable convexly constrained optimization with non-Lipschitzian singularities (2019)
  14. Dempe, S.; Franke, S.: Solution of bilevel optimization problems using the KKT approach (2019)
  15. Gambella, Claudio; Maggioni, Francesca; Vigo, Daniele: A stochastic programming model for a tactical solid waste management problem (2019)
  16. Goodall, Gavin; Scioletti, Michael; Zolan, Alex; Suthar, Bharatkumar; Newman, Alexandra; Kohl, Paul: Optimal design and dispatch of a hybrid microgrid system capturing battery fade (2019)
  17. Johnson, Benjamin L.; Porter, Aaron T.; King, Jeffrey C.; Newman, Alexandra M.: Optimally configuring a measurement system to detect diversions from a nuclear fuel cycle (2019)
  18. Kaya, C. Yalçın: Markov-Dubins interpolating curves (2019)
  19. Kim, Youngdae; Ferris, Michael C.: Solving equilibrium problems using extended mathematical programming (2019)
  20. Ledzewicz, Urszula; Maurer, Helmut; Schättler, Heinz: Optimal combined radio- and anti-angiogenic cancer therapy (2019)

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