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 624 articles , 2 standard articles )

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  1. Bienstock, Daniel; Escobar, Mauro; Gentile, Claudio; Liberti, Leo: Mathematical programming formulations for the alternating current optimal power flow problem (2022)
  2. Gorissen, Bram L.: Interior point methods can exploit structure of convex piecewise linear functions with application in radiation therapy (2022)
  3. Hurst, Todd; Rehbock, Volker: Optimizing micro-algae production in a raceway pond with variable depth (2022)
  4. Kaya, C. Yalçın: Observer path planning for maximum information (2022)
  5. Kettunen, Janne; Lejeune, Miguel A.: Data-driven project portfolio selection: decision-dependent stochastic programming formulations with reliability and time to market requirements (2022)
  6. Legat, Benoît; Dowson, Oscar; Garcia, Joaquim Dias; Lubin, Miles: MathOptInterface: a data structure for mathematical optimization problems (2022)
  7. Liberti, Leo; Manca, Benedetto: Side-constrained minimum sum-of-squares clustering: mathematical programming and random projections (2022)
  8. Lundell, Andreas; Kronqvist, Jan; Westerlund, Tapio: The supporting hyperplane optimization toolkit for convex MINLP (2022)
  9. Martins Barros, Rafael; Guimarães Lage, Guilherme; de Andrade Lira Rabêlo, Ricardo: Sequencing paths of optimal control adjustments determined by the optimal reactive dispatch via Lagrange multiplier sensitivity analysis (2022)
  10. Maskooki, Alaleh; Deb, Kalyanmoy; Kallio, Markku: A customized genetic algorithm for bi-objective routing in a dynamic network (2022)
  11. Monteiro, M. Teresa T.; Espírito Santo, Isabel; Rodrigues, Helena Sofia: An optimal control problem applied to a wastewater treatment plant (2022)
  12. Nath, Bhagya Jyoti; Sarmah, Hemanta Kumar; Maurer, Helmut: An optimal control strategy for antiretroviral treatment of HIV infection in presence of immunotherapy (2022)
  13. Silva, Cristiana J.: Stability and optimal control of a delayed HIV/AIDS-PrEP model (2022)
  14. Tangi Migot; Dominique Orban; Abel Soares Siqueira: DCISolver.jl: A Julia Solver for Nonlinear Optimization using Dynamic Control of Infeasibility (2022) not zbMATH
  15. Aftalion, Amandine; Trélat, Emmanuel: Pace and motor control optimization for a runner (2021)
  16. Burachik, Regina S.; Kalloniatis, Alexander C.; Kaya, C. Yalçın: Sparse network optimization for synchronization (2021)
  17. Cerulli, Martina; D’Ambrosio, Claudia; Liberti, Leo; Pelegrín, Mercedes: Detecting and solving aircraft conflicts using bilevel programming (2021)
  18. Chen, X.; Toint, Ph. L.: High-order evaluation complexity for convexly-constrained optimization with non-Lipschitzian group sparsity terms (2021)
  19. Dias, Gustavo; Liberti, Leo: Exploiting symmetries in mathematical programming via orbital independence (2021)
  20. Ding Ma, Dominique Orban, Michael A. Saunders: A Julia implementation of Algorithm NCL for constrained optimization (2021) arXiv

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