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 30 articles )

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  1. Beyhaghi, Pooriya; Bewley, Thomas R.: Delaunay-based derivative-free optimization via global surrogates. II: Convex constraints (2016)
  2. Durantin, Cédric; Marzat, Julien; Balesdent, Mathieu: Analysis of multi-objective Kriging-based methods for constrained global optimization (2016)
  3. Audet, Charles; Le Digabel, Sébastien; Peyrega, Mathilde: Linear equalities in blackbox optimization (2015)
  4. Burmen, Árpád; Olenšek, Jernej; Tuma, Tadej: Mesh adaptive direct search with second directional derivative-based Hessian update (2015)
  5. Diouane, Y.; Gratton, S.; Vicente, L.N.: Globally convergent evolution strategies (2015)
  6. Diouane, Y.; Gratton, S.; Vicente, L.N.: Globally convergent evolution strategies for constrained optimization (2015)
  7. Gould, Nicholas I.M.; Orban, Dominique; Toint, Philippe L.: CUTEst: a constrained and unconstrained testing environment with safe threads for mathematical optimization (2015)
  8. 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)
  9. Grippo, L.; Rinaldi, F.: A class of derivative-free nonmonotone optimization algorithms employing coordinate rotations and gradient approximations (2015)
  10. Hall, Peter G.; Racine, Jeffrey S.: Infinite order cross-validated local polynomial regression (2015)
  11. Liuzzi, Giampaolo; Lucidi, Stefano; Rinaldi, Francesco: Derivative-free methods for mixed-integer constrained optimization problems (2015)
  12. Newby, Eric; Ali, M.M.: A trust-region-based derivative free algorithm for mixed integer programming (2015)
  13. Adjengue, Luc; Audet, Charles; Ben Yahia, Imen: A variance-based method to rank input variables of the mesh adaptive direct search algorithm (2014)
  14. Audet, Charles; Dang, Kien-Cong; Orban, Dominique: Optimization of algorithms with OPAL (2014)
  15. Müller, Juliane; Shoemaker, Christine A.: Influence of ensemble surrogate models and sampling strategy on the solution quality of algorithms for computationally expensive black-box global optimization problems (2014)
  16. Müller, Juliane; Shoemaker, Christine A.; Piché, Robert: SO-I: a surrogate model algorithm for expensive nonlinear integer programming problems including global optimization applications (2014)
  17. Alarie, Stéphane; Audet, Charles; Garnier, Vincent; Le Digabel, Sébastien; Leclaire, Louis-Alexandre: Snow water equivalent estimation using blackbox optimization (2013)
  18. Audet, C.; Dang, C.-K.; Orban, D.: Efficient use of parallelism in algorithmic parameter optimization applications (2013)
  19. Conn, Andrew R.; Le Digabel, Sébastien: Use of quadratic models with mesh-adaptive direct search for constrained black box optimization (2013)
  20. Hare, W.; Nutini, J.: A derivative-free approximate gradient sampling algorithm for finite minimax problems (2013)

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