Numerical approximation of the solution in infinite dimensional global optimization using a representation formula
A non convex optimization problem, involving a regular functional J , on a closed and bounded subset S of a separable Hilbert space V is here considered. No convexity assumption is introduced. The solutions are represented by using a closed formula involving means of convenient random variables, ana...
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| Veröffentlicht in: | Journal of global optimization Jg. 65; H. 2; S. 261 - 281 |
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| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
New York
Springer US
01.06.2016
Springer Springer Nature B.V |
| Schlagworte: | |
| ISSN: | 0925-5001, 1573-2916 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | A non convex optimization problem, involving a regular functional
J
, on a closed and bounded subset
S
of a separable Hilbert space
V
is here considered. No convexity assumption is introduced. The solutions are represented by using a closed formula involving means of convenient random variables, analogous to Pincus (Oper Res 16(3):690–694,
1968
). The representation suggests a numerical method based on the generation of samples in order to estimate the means. Three strategies for the implementation are examined, with the originality that they do not involve a priori finite dimensional approximation of the solution and consider a hilbertian basis or enumerable dense family of
V
. The results may be improved on a finite-dimensional subspace by an optimization procedure, in order to get higher-quality solutions. Numerical examples involving both classical situation and an engineering application issued from calculus of variations are presented and establish that the method is effective to calculate. |
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| Bibliographie: | SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-1 ObjectType-Feature-2 content type line 23 |
| ISSN: | 0925-5001 1573-2916 |
| DOI: | 10.1007/s10898-015-0357-5 |