BFO

BFO, a trainable derivative-free brute force optimizer for nonlinear bound-constrained optimization and equilibrium computations with continuous and discrete variables. A direct-search derivative-free Matlab optimizer for bound-constrained problems is described, whose remarkable features are its ability to handle a mix of continuous and discrete varibles, a versatile interface as well as a novel self-training option. Its performance compares favorable with that of NOMAD (Nonsmooth Optimization by Mesh Adaptive Direct Search), a well-known derivative-free optimization package. It is also applicable to multilevel equilibrium- or constrained-type problems. Its easy-to-use interface provides a number of user-oriented features, such as checkpointing and restart, variable scaling, and early termination tools.


References in zbMATH (referenced in 11 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. Lakhmiri, Dounia; Digabel, S├ębastien Le; Tribes, Christophe: HyperNOMAD. Hyperparameter optimization of deep neural networks using mesh adaptive direct search (2021)
  4. Larson, Jeffrey; Leyffer, Sven; Palkar, Prashant; Wild, Stefan M.: A method for convex black-box integer global optimization (2021)
  5. 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)
  6. 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)
  7. Audet, Charles; Le Digabel, S├ębastien; Tribes, Christophe: The mesh adaptive direct search algorithm for granular and discrete variables (2019)
  8. Larson, Jeffrey; Menickelly, Matt; Wild, Stefan M.: Derivative-free optimization methods (2019)
  9. Chen, Xiaojun; Kelley, C. T.; Xu, Fengmin; Zhang, Zaikun: A smoothing direct search method for Monte Carlo-based bound constrained composite nonsmooth optimization (2018)
  10. Marini, Leopoldo; Morini, Benedetta; Porcelli, Margherita: Quasi-Newton methods for constrained nonlinear systems: complexity analysis and applications (2018)
  11. Porcelli, Margherita; Toint, Philippe L.: BFO, a trainable derivative-free brute force optimizer for nonlinear bound-constrained optimization and equilibrium computations with continuous and discrete variables (2017)