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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

Margherita Porcelli (margherita.porcelli***at***unifi.it)
Philippe L. Toint (philippe.toint***at***unamur.be)

Abstract: 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 checkpointing and restart, variable scaling and early termination tools.

Keywords: derivative-free optimization, direct-search methods, mixed integer optimization, bound constraints, trainable algorithms

Category 1: Nonlinear Optimization

Category 2: Nonlinear Optimization (Bound-constrained Optimization )

Category 3: Integer Programming ((Mixed) Integer Nonlinear Programming )

Citation: ACM Transactions on Mathematical Software 44:1 (2017), Article 6, 25 pages.

Download: [PDF]

Entry Submitted: 07/03/2015
Entry Accepted: 07/03/2015
Entry Last Modified: 07/13/2017

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