NOMAD

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 61 articles )

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  1. Amaioua, Nadir; Audet, Charles; Conn, Andrew R.; Le Digabel, Sébastien: Efficient solution of quadratically constrained quadratic subproblems within the mesh adaptive direct search algorithm (2018)
  2. Audet, Charles; Ihaddadene, Amina; Le Digabel, Sébastien; Tribes, Christophe: Robust optimization of noisy blackbox problems using the mesh adaptive direct search algorithm (2018)
  3. Audet, Charles; Kokkolaras, Michael; Le Digabel, Sébastien; Talgorn, Bastien: Order-based error for managing ensembles of surrogates in mesh adaptive direct search (2018)
  4. Marx, David: A piecewise linear contour to avoid critical points in inviscid flow stability analyses (2018)
  5. Nedělková, Zuzana; Lindroth, Peter; Patriksson, Michael; Strömberg, Ann-Brith: Efficient solution of many instances of a simulation-based optimization problem utilizing a partition of the decision space (2018)
  6. Nuñez, Luigi; Regis, Rommel G.; Varela, Kayla: Accelerated random search for constrained global optimization assisted by radial basis function surrogates (2018)
  7. Talgorn, Bastien; Audet, Charles; Le Digabel, Sébastien; Kokkolaras, Michael: Locally weighted regression models for surrogate-assisted design optimization (2018)
  8. Boukouvala, Fani; Faruque Hasan, M. M.; Floudas, Christodoulos A.: Global optimization of general constrained grey-box models: new method and its application to constrained PDEs for pressure swing adsorption (2017)
  9. Boukouvala, Fani; Floudas, Christodoulos A.: ARGONAUT: algorithms for global optimization of constrained grey-box computational problems (2017)
  10. Müller, Juliane; Woodbury, Joshua D.: GOSAC: global optimization with surrogate approximation of constraints (2017)
  11. Regis, Rommel G.; Wild, Stefan M.: CONORBIT: constrained optimization by radial basis function interpolation in trust regions (2017)
  12. Vu, Ky Khac; D’Ambrosio, Claudia; Hamadi, Youssef; Liberti, Leo: Surrogate-based methods for black-box optimization (2017)
  13. Armstrong, Jerawan C.; Favorite, Jeffrey A.: Using a derivative-free optimization method for multiple solutions of inverse transport problems (2016)
  14. Audet, Charles; Le Digabel, Sébastien; Tribes, Christophe: Dynamic scaling in the mesh adaptive direct search algorithm for blackbox optimization (2016)
  15. Beyhaghi, Pooriya; Bewley, Thomas R.: Delaunay-based derivative-free optimization via global surrogates. II: Convex constraints (2016)
  16. Bigdeli, K.; Hare, W.; Nutini, J.; Tesfamariam, S.: Optimizing damper connectors for adjacent buildings (2016)
  17. Boukouvala, Fani; Misener, Ruth; Floudas, Christodoulos A.: Global optimization advances in mixed-integer nonlinear programming, MINLP, and constrained derivative-free optimization, CDFO (2016)
  18. Csercsik, Dávid: Competition and cooperation in a bidding model of electrical energy trade (2016)
  19. Csercsik, Dávid: Lying generators: manipulability of centralized payoff mechanisms in electrical energy trade (2016)
  20. Duhamel, Christophe; Santos, Andréa Cynthia; Brasil, Daniel; Ch^atelet, Eric; Birregah, Babiga: Connecting a population dynamic model with a multi-period location-allocation problem for post-disaster relief operations (2016)

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