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BFO, A Trainable Derivative-free Brute Force Optimizer for Nonlinear Bound-constrained Optimization and Equilibrium Computations with Continuous and Discrete Variables
2017
ACM Transactions on Mathematical Software
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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 variables, a versatile interface as well as a novel self-training option. Its performance compares favourably with that of NOMAD, a state-of-the art 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
doi:10.1145/3085592
fatcat:vtb6nsdotjautphxsmzauynxnm