BFO, a trainable derivative-free Brute Force Optimizer for nonlinear bound-constrained optimization and equilibrium computations with continuous and discrete variables
Margherita Porcelli (margherita.porcelliunifi.it)
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.
Entry Submitted: 07/03/2015
Modify/Update this entry
|Visitors||Authors||More about us||Links|
Search, Browse the Repository
Give us feedback
|Optimization Journals, Sites, Societies|