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 76 articles )

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  1. Audet, Charles; Côté, Pascal; Poissant, Catherine; Tribes, Christophe: Monotonic grey box direct search optimization (2020)
  2. Audet, Charles; Côté-Massicotte, Julien: Dynamic improvements of static surrogates in direct search optimization (2019)
  3. Audet, Charles; Le Digabel, Sébastien; Tribes, Christophe: The mesh adaptive direct search algorithm for granular and discrete variables (2019)
  4. Berahas, Albert S.; Byrd, Richard H.; Nocedal, Jorge: Derivative-free optimization of noisy functions via quasi-Newton methods (2019)
  5. Gratton, S.; Royer, C. W.; Vicente, L. N.; Zhang, Z.: Direct search based on probabilistic feasible descent for bound and linearly constrained problems (2019)
  6. Larson, Jeffrey; Menickelly, Matt; Wild, Stefan M.: Derivative-free optimization methods (2019)
  7. Liuzzi, Giampaolo; Lucidi, Stefano; Rinaldi, Francesco; Vicente, Luis Nunes: Trust-region methods for the derivative-free optimization of nonsmooth black-box functions (2019)
  8. Sanguinetti, Guido (ed.); Huynh-Thu, Vân Anh (ed.): Gene regulatory networks. Methods and protocols (2019)
  9. 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)
  10. Audet, Charles; Conn, Andrew R.; Le Digabel, Sébastien; Peyrega, Mathilde: A progressive barrier derivative-free trust-region algorithm for constrained optimization (2018)
  11. Audet, Charles; Ihaddadene, Amina; Le Digabel, Sébastien; Tribes, Christophe: Robust optimization of noisy blackbox problems using the mesh adaptive direct search algorithm (2018)
  12. Audet, Charles; Kokkolaras, Michael; Le Digabel, Sébastien; Talgorn, Bastien: Order-based error for managing ensembles of surrogates in mesh adaptive direct search (2018)
  13. Audet, Charles; Tribes, Christophe: Mesh-based Nelder-Mead algorithm for inequality constrained optimization (2018)
  14. Costa, Alberto; Nannicini, Giacomo: RBFOpt: an open-source library for black-box optimization with costly function evaluations (2018)
  15. Hare, Warren; Loeppky, Jason; Xie, Shangwei: Methods to compare expensive stochastic optimization algorithms with random restarts (2018)
  16. Liuzzi, G.; Truemper, K.: Parallelized hybrid optimization methods for nonsmooth problems using NOMAD and linesearch (2018)
  17. Marx, David: A piecewise linear contour to avoid critical points in inviscid flow stability analyses (2018)
  18. 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)
  19. Nuñez, Luigi; Regis, Rommel G.; Varela, Kayla: Accelerated random search for constrained global optimization assisted by radial basis function surrogates (2018)
  20. Sarker, Bhaba R.; Wu, Bingqing; Paudel, Krishna P.: Optimal number and location of storage hubs and biogas production reactors in farmlands with allocation of multiple feedstocks (2018)

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