Résumé
| langue originale | Anglais |
|---|---|
| Pages (de - à) | 479-491 |
| Nombre de pages | 13 |
| journal | Optimization Methods and Software |
| Volume | 21 |
| Numéro de publication | 3 |
| Les DOIs | |
| état | Publié - 1 juin 2006 |
Empreinte digitale
Citer ceci
}
Exploiting problem structure in pattern search methods for unconstrained optimization. / Price, Chris; Toint, Philippe.
Dans: Optimization Methods and Software, Vol 21, Numéro 3, 01.06.2006, p. 479-491.Résultats de recherche: Contribution à un journal/une revue › Article
TY - JOUR
T1 - Exploiting problem structure in pattern search methods for unconstrained optimization
AU - Price, Chris
AU - Toint, Philippe
N1 - Publication code : #QA 0002.2/001/04/01 ; *FP SB010/2004/01
PY - 2006/6/1
Y1 - 2006/6/1
N2 - A direct search method for unconstrained optimization is described. The method makes use of any partial separability structure that the objective function may have. The method uses successively finer nested grids, and minimizes the objective function over each grid in turn. All grids are aligned with the coordinate directions, which allows the partial separability structure of the objective function to be exploited. This has two advantages: it reduces the work needed to calculate function values at the points required and it provides function values at other points as a free by-product. Numerical results show that using partial separability can dramatically reduce the number of function evaluations needed to minimize a function, in some cases allowing problems with thousands of variables to be solved. Results show that the algorithm is effective on strictly C 1 problems and on a class of non-smooth problems.
AB - A direct search method for unconstrained optimization is described. The method makes use of any partial separability structure that the objective function may have. The method uses successively finer nested grids, and minimizes the objective function over each grid in turn. All grids are aligned with the coordinate directions, which allows the partial separability structure of the objective function to be exploited. This has two advantages: it reduces the work needed to calculate function values at the points required and it provides function values at other points as a free by-product. Numerical results show that using partial separability can dramatically reduce the number of function evaluations needed to minimize a function, in some cases allowing problems with thousands of variables to be solved. Results show that the algorithm is effective on strictly C 1 problems and on a class of non-smooth problems.
KW - derivative-free
KW - pattern search
KW - numerical results
KW - partial separability
UR - http://www.scopus.com/inward/record.url?scp=30944446857&partnerID=8YFLogxK
U2 - 10.1080/10556780500137116
DO - 10.1080/10556780500137116
M3 - Article
VL - 21
SP - 479
EP - 491
JO - Optimization Methods and Software
JF - Optimization Methods and Software
SN - 1055-6788
IS - 3
ER -