DFL

Derivative-free methods for bound constrained mixed-integer optimization We consider the problem of minimizing a continuously differentiable function of several variables subject to simple bound constraints where some of the variables are restricted to take integer values. We assume that the first order derivatives of the objective function can be neither calculated nor approximated explicitly. This class of mixed integer nonlinear optimization problems arises frequently in many industrial and scientific applications and this motivates the increasing interest in the study of derivative-free methods for their solution. The continuous variables are handled by a linesearch strategy whereas to tackle the discrete ones we employ a local search-type approach. We propose different algorithms which are characterized by the way the current iterate is updated and by the stationarity conditions satisfied by the limit points of the sequences they produce. (Source: http://plato.asu.edu)


References in zbMATH (referenced in 30 articles , 1 standard article )

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  1. De Santis, Alberto; Dellepiane, Umberto; Lucidi, Stefano; Renzi, Stefania: A derivative-free optimization approach for the autotuning of a forex trading strategy (2021)
  2. Lapucci, Matteo; Levato, Tommaso; Sciandrone, Marco: Convergent inexact penalty decomposition methods for cardinality-constrained problems (2021)
  3. Audet, Charles; Caporossi, Gilles; Jacquet, Stéphane: Binary, unrelaxable and hidden constraints in blackbox optimization (2020)
  4. Diniz-Ehrhardt, M. A.; Ferreira, D. G.; Santos, S. A.: Applying the pattern search implicit filtering algorithm for solving a noisy problem of parameter identification (2020)
  5. García-Palomares, Ubaldo M.: Non-monotone derivative-free algorithm for solving optimization models with linear constraints: extensions for solving nonlinearly constrained models via exact penalty methods (2020)
  6. Gaudioso, Manlio; Giallombardo, Giovanni; Miglionico, Giovanna: Essentials of numerical nonsmooth optimization (2020)
  7. Helou, Elias S.; Santos, Sandra A.; Simões, Lucas E. A.: A new sequential optimality condition for constrained nonsmooth optimization (2020)
  8. 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)
  9. Audet, Charles; Le Digabel, Sébastien; Tribes, Christophe: The mesh adaptive direct search algorithm for granular and discrete variables (2019)
  10. Bagirov, A. M.; Ozturk, G.; Kasimbeyli, Refail: A sharp augmented Lagrangian-based method in constrained non-convex optimization (2019)
  11. Bruni, Renato; Celani, Fabio: Combining global and local strategies to optimize parameters in magnetic spacecraft control via attitude feedback (2019)
  12. Diniz-Ehrhardt, M. A.; Ferreira, D. G.; Santos, S. A.: A pattern search and implicit filtering algorithm for solving linearly constrained minimization problems with noisy objective functions (2019)
  13. García-Palomares, Ubaldo M.; Rodríguez-Hernández, Pedro S.: Unified approach for solving box-constrained models with continuous or discrete variables by non monotone direct search methods (2019)
  14. Larson, Jeffrey; Menickelly, Matt; Wild, Stefan M.: Derivative-free optimization methods (2019)
  15. Latorre, Vittorio; Habal, Husni; Graeb, Helmut; Lucidi, Stefano: Derivative free methodologies for circuit worst case analysis (2019)
  16. Liuzzi, Giampaolo; Lucidi, Stefano; Rinaldi, Francesco; Vicente, Luis Nunes: Trust-region methods for the derivative-free optimization of nonsmooth black-box functions (2019)
  17. Costa, M. Fernanda P.; Rocha, Ana Maria A. C.; Fernandes, Edite M. G. P.: Filter-based DIRECT method for constrained global optimization (2018)
  18. Liuzzi, G.; Truemper, K.: Parallelized hybrid optimization methods for nonsmooth problems using NOMAD and linesearch (2018)
  19. Boukouvala, Fani; Misener, Ruth; Floudas, Christodoulos A.: Global optimization advances in mixed-integer nonlinear programming, MINLP, and constrained derivative-free optimization, CDFO (2016)
  20. Di Pillo, G.; Liuzzi, G.; Lucidi, S.; Piccialli, V.; Rinaldi, F.: A DIRECT-type approach for derivative-free constrained global optimization (2016)

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