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 favorably 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.
| Original language | English |
|---|---|
| Article number | 6 |
| Number of pages | 28 |
| Journal | Transactions of the American Methematical Society on Mathematical Software |
| Volume | 44 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 30 Jun 2017 |
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Keywords
- derivative-free optimization
- trainable algorithms
- bound constraints
- mixed integer optimization
- direct-search methods
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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. / Porcelli, Margherita; Toint, Philippe.
In: Transactions of the American Methematical Society on Mathematical Software, Vol. 44, No. 1, 6, 30.06.2017.Research output: Contribution to journal › Article
TY - JOUR
T1 - BFO, a trainable derivative-free Brute Force Optimizer for nonlinear bound-constrained optimization and equilibrium computations with continuous and discrete variables
AU - Porcelli, Margherita
AU - Toint, Philippe
PY - 2017/6/30
Y1 - 2017/6/30
N2 - 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 favorably 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.
AB - 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 favorably 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.
KW - derivative-free optimization
KW - trainable algorithms
KW - bound constraints
KW - mixed integer optimization
KW - direct-search methods
UR - http://www.scopus.com/inward/record.url?scp=85024481256&partnerID=8YFLogxK
U2 - 10.1145/3085592
DO - 10.1145/3085592
M3 - Article
VL - 44
JO - Transactions of the American Methematical Society on Mathematical Software
JF - Transactions of the American Methematical Society on Mathematical Software
SN - 0002-9947
IS - 1
M1 - 6
ER -