Algorithm 909: NOMAD: Nonlinear Optimization with the MADS Algorithm. NOMAD is software that implements the Mesh Adaptive Direct Search (MADS) algorithm for blackbox optimization under general nonlinear constraints. Blackbox optimization is about optimizing functions that are usually given as costly programs with no derivative information and no function values returned for a significant number of calls attempted. NOMAD is designed for such problems and aims for the best possible solution with a small number of evaluations. The objective of this article is to describe the underlying algorithm, the software’s functionalities, and its implementation.

References in zbMATH (referenced in 105 articles )

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  1. Butyn, Emerson; Karas, Elizabeth W.; de Oliveira, Welington: A derivative-free trust-region algorithm with copula-based models for probability maximization problems (2022)
  2. Giuliani, Caio Merlini; Camponogara, Eduardo; Conn, Andrew R.: A derivative-free exact penalty algorithm: basic ideas, convergence theory and computational studies (2022)
  3. Lakhmiri, Dounia; Le Digabel, Sébastien: Use of static surrogates in hyperparameter optimization (2022)
  4. Ploskas, Nikolaos; Sahinidis, Nikolaos V.: Review and comparison of algorithms and software for mixed-integer derivative-free optimization (2022)
  5. Alarie, Stéphane; Audet, Charles; Bouchet, Pierre-Yves; Digabel, Sébastien Le: Optimization of stochastic blackboxes with adaptive precision (2021)
  6. Audet, Charles; Bigeon, Jean; Couderc, Romain: Combining cross-entropy and MADS methods for inequality constrained global optimization (2021)
  7. Audet, Charles; Dzahini, Kwassi Joseph; Kokkolaras, Michael; Le Digabel, Sébastien: Stochastic mesh adaptive direct search for blackbox optimization using probabilistic estimates (2021)
  8. Galvan, Giulio; Sciandrone, Marco; Lucidi, Stefano: A parameter-free unconstrained reformulation for nonsmooth problems with convex constraints (2021)
  9. Lakhmiri, Dounia; Digabel, Sébastien Le; Tribes, Christophe: HyperNOMAD. Hyperparameter optimization of deep neural networks using mesh adaptive direct search (2021)
  10. Larson, Jeffrey; Leyffer, Sven; Palkar, Prashant; Wild, Stefan M.: A method for convex black-box integer global optimization (2021)
  11. Müller, Juliane; Park, Jangho; Sahu, Reetik; Varadharajan, Charuleka; Arora, Bhavna; Faybishenko, Boris; Agarwal, Deborah: Surrogate optimization of deep neural networks for groundwater predictions (2021)
  12. Sampaio, Phillipe R.: DEFT-FUNNEL: an open-source global optimization solver for constrained grey-box and black-box problems (2021)
  13. Xia, Wei; Shoemaker, Christine: GOPS: efficient RBF surrogate global optimization algorithm with high dimensions and many parallel processors including application to multimodal water quality PDE model calibration (2021)
  14. Alimo, Ryan; Beyhaghi, Pooriya; Bewley, Thomas R.: Delaunay-based derivative-free optimization via global surrogates. III: nonconvex constraints (2020)
  15. Audet, Charles; Côté, Pascal; Poissant, Catherine; Tribes, Christophe: Monotonic grey box direct search optimization (2020)
  16. Bajaj, Ishan; Hasan, M. M. Faruque: Global dynamic optimization using edge-concave underestimator (2020)
  17. Bhosekar, Atharv; Ierapetritou, Marianthi: A discontinuous derivative-free optimization framework for multi-enterprise supply chain (2020)
  18. Cocchi, Guido; Levato, Tommaso; Liuzzi, Giampaolo; Sciandrone, Marco: A concave optimization-based approach for sparse multiobjective programming (2020)
  19. García-Palomares, Ubaldo M.: Non-monotone derivative-free algorithm for solving optimization models with linear constraints: extensions for solving nonlinearly constrained models via exact penalty methods (2020)
  20. Jiang, Su; Sun, Wenyue; Durlofsky, Louis J.: A data-space inversion procedure for well control optimization and closed-loop reservoir management (2020)

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