DFL - A Derivative-Free Library - DFLBOX: 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.

References in zbMATH (referenced in 14 articles , 1 standard article )

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  1. Ploskas, Nikolaos; Sahinidis, Nikolaos V.: Review and comparison of algorithms and software for mixed-integer derivative-free optimization (2022)
  2. De Santis, Alberto; Dellepiane, Umberto; Lucidi, Stefano; Renzi, Stefania: A derivative-free optimization approach for the autotuning of a forex trading strategy (2021)
  3. Larson, Jeffrey; Leyffer, Sven; Palkar, Prashant; Wild, Stefan M.: A method for convex black-box integer global optimization (2021)
  4. 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)
  5. Audet, Charles; Le Digabel, Sébastien; Tribes, Christophe: The mesh adaptive direct search algorithm for granular and discrete variables (2019)
  6. 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)
  7. Larson, Jeffrey; Menickelly, Matt; Wild, Stefan M.: Derivative-free optimization methods (2019)
  8. Boukouvala, Fani; Misener, Ruth; Floudas, Christodoulos A.: Global optimization advances in mixed-integer nonlinear programming, MINLP, and constrained derivative-free optimization, CDFO (2016)
  9. Lucidi, Stefano; Maurici, Massimo; Paulon, Luca; Rinaldi, Francesco; Roma, Massimo: A derivative-free approach for a simulation-based optimization problem in healthcare (2016)
  10. Ciccazzo, Angelo; Latorre, Vittorio; Liuzzi, Giampaolo; Lucidi, Stefano; Rinaldi, Francesco: Derivative-free robust optimization for circuit design (2015)
  11. Grippo, L.; Rinaldi, F.: A class of derivative-free nonmonotone optimization algorithms employing coordinate rotations and gradient approximations (2015)
  12. Liuzzi, Giampaolo; Lucidi, Stefano; Rinaldi, Francesco: Derivative-free methods for mixed-integer constrained optimization problems (2015)
  13. Lv, Wei; Sun, Qiang; Lin, He; Sui, Ruirui: A penalty derivative-free algorithm for nonlinear constrained optimization (2015)
  14. Liuzzi, G.; Lucidi, S.; Rinaldi, F.: Derivative-free methods for bound constrained mixed-integer optimization (2012)