OrthoMADS

Orthomads: A deterministic MADS instance with orthogonal directions. The purpose of this paper is to introduce a new way of choosing directions for the mesh adaptive direct search (Mads) class of algorithms. The advantages of this new OrthoMads instantiation of Mads are that the polling directions are chosen deterministically, ensuring that the results of a given run are repeatable, and that they are orthogonal to each other, which yields convex cones of missed directions at each iteration that are minimal in a reasonable measure. Convergence results for OrthoMads follow directly from those already published for Mads, and they hold deterministically, rather than with probability one, as is the case for LtMads, the first Mads instance. The initial numerical results are quite good for both smooth and nonsmooth and constrained and unconstrained problems considered here.


References in zbMATH (referenced in 55 articles )

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  1. Audet, Charles; Dzahini, Kwassi Joseph; Kokkolaras, Michael; Le Digabel, Sébastien: Stochastic mesh adaptive direct search for blackbox optimization using probabilistic estimates (2021)
  2. Alimo, Ryan; Beyhaghi, Pooriya; Bewley, Thomas R.: Delaunay-based derivative-free optimization via global surrogates. III: nonconvex constraints (2020)
  3. Audet, Charles; Caporossi, Gilles; Jacquet, Stéphane: Binary, unrelaxable and hidden constraints in blackbox optimization (2020)
  4. Hare, Warren; Jarry-Bolduc, Gabriel: A deterministic algorithm to compute the cosine measure of a finite positive spanning set (2020)
  5. Hare, Warren; Planiden, Chayne; Sagastizábal, Claudia: A derivative-free (\mathcalV\mathcalU)-algorithm for convex finite-max problems (2020)
  6. Jarry-Bolduc, Gabriel; Nadeau, Patrick; Singh, Shambhavi: Uniform simplex of an arbitrary orientation (2020)
  7. Liuzzi, Giampaolo; Lucidi, Stefano; Rinaldi, Francesco: An algorithmic framework based on primitive directions and nonmonotone line searches for black-box optimization problems with integer variables (2020)
  8. Manno, Andrea; Amaldi, Edoardo; Casella, Francesco; Martelli, Emanuele: A local search method for costly black-box problems and its application to CSP plant start-up optimization refinement (2020)
  9. Verma, Aekaansh; Wong, Kwai; Marsden, Alison L.: A concurrent implementation of the surrogate management framework with application to cardiovascular shape optimization (2020)
  10. Audet, Charles; Côté-Massicotte, Julien: Dynamic improvements of static surrogates in direct search optimization (2019)
  11. Bei, Xiaoqiang; Zhu, Xiaoyan; Coit, David W.: A risk-averse stochastic program for integrated system design and preventive maintenance planning (2019)
  12. Bűrmen, Árpád; Fajfar, Iztok: Mesh adaptive direct search with simplicial Hessian update (2019)
  13. Larson, Jeffrey; Menickelly, Matt; Wild, Stefan M.: Derivative-free optimization methods (2019)
  14. Latorre, Vittorio; Habal, Husni; Graeb, Helmut; Lucidi, Stefano: Derivative free methodologies for circuit worst case analysis (2019)
  15. Müller, Juliane; Day, Marcus: Surrogate optimization of computationally expensive black-box problems with hidden constraints (2019)
  16. Audet, Charles; Ihaddadene, Amina; Le Digabel, Sébastien; Tribes, Christophe: Robust optimization of noisy blackbox problems using the mesh adaptive direct search algorithm (2018)
  17. Audet, Charles; Kokkolaras, Michael; Le Digabel, Sébastien; Talgorn, Bastien: Order-based error for managing ensembles of surrogates in mesh adaptive direct search (2018)
  18. Dreisigmeyer, David W.: Direct search methods on reductive homogeneous spaces (2018)
  19. Liuzzi, G.; Truemper, K.: Parallelized hybrid optimization methods for nonsmooth problems using NOMAD and linesearch (2018)
  20. Beyhaghi, Pooriya; Bewley, Thomas: Implementation of Cartesian grids to accelerate Delaunay-based derivative-free optimization (2017)

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