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 37 articles )

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  1. 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)
  2. Beyhaghi, Pooriya; Bewley, Thomas R.: Delaunay-based derivative-free optimization via global surrogates. II: Convex constraints (2016)
  3. Boukouvala, Fani; Misener, Ruth; Floudas, Christodoulos A.: Global optimization advances in mixed-integer nonlinear programming, MINLP, and constrained derivative-free optimization, CDFO (2016)
  4. Csercsik, Dávid: Lying generators: manipulability of centralized payoff mechanisms in electrical energy trade (2016)
  5. 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)
  6. Durantin, Cédric; Marzat, Julien; Balesdent, Mathieu: Analysis of multi-objective Kriging-based methods for constrained global optimization (2016)
  7. Fusi, F.; Congedo, P.M.: An adaptive strategy on the error of the objective functions for uncertainty-based derivative-free optimization (2016)
  8. Audet, Charles; Le Digabel, Sébastien; Peyrega, Mathilde: Linear equalities in blackbox optimization (2015)
  9. Burmen, Árpád; Olenšek, Jernej; Tuma, Tadej: Mesh adaptive direct search with second directional derivative-based Hessian update (2015)
  10. Diouane, Y.; Gratton, S.; Vicente, L.N.: Globally convergent evolution strategies for constrained optimization (2015)
  11. Diouane, Y.; Gratton, S.; Vicente, L.N.: Globally convergent evolution strategies (2015)
  12. Gould, Nicholas I.M.; Orban, Dominique; Toint, Philippe L.: CUTEst: a constrained and unconstrained testing environment with safe threads for mathematical optimization (2015)
  13. Gramacy, Robert B.; Bingham, Derek; Holloway, James Paul; Grosskopf, Michael J.; Kuranz, Carolyn C.; Rutter, Erica; Trantham, Matt; Drake, R.Paul: Calibrating a large computer experiment simulating radiative shock hydrodynamics (2015)
  14. Grippo, L.; Rinaldi, F.: A class of derivative-free nonmonotone optimization algorithms employing coordinate rotations and gradient approximations (2015)
  15. Hall, Peter G.; Racine, Jeffrey S.: Infinite order cross-validated local polynomial regression (2015)
  16. Liuzzi, Giampaolo; Lucidi, Stefano; Rinaldi, Francesco: Derivative-free methods for mixed-integer constrained optimization problems (2015)
  17. Newby, Eric; Ali, M.M.: A trust-region-based derivative free algorithm for mixed integer programming (2015)
  18. Voglis, C.; Hadjidoukas, P.E.; Parsopoulos, K.E.; Papageorgiou, D.G.; Lagaris, I.E.; Vrahatis, M.N.: p-MEMPSODE: parallel and irregular memetic global optimization (2015)
  19. Adjengue, Luc; Audet, Charles; Ben Yahia, Imen: A variance-based method to rank input variables of the mesh adaptive direct search algorithm (2014)
  20. Audet, Charles; Dang, Kien-Cong; Orban, Dominique: Optimization of algorithms with OPAL (2014)

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