TY  - JOUR
EP  - 106
PB  - Springer
VL  - 9
IS  - 1
JF  - 4OR: A Quarterly Journal of Operations Research
N2  - This is a summary of the author?s PhD thesis, supervised by Marcello Sanguineti and defended on April 2, 2009 at Università degli Studi di Genova. The thesis is written in English and a copy is available from the author upon request. Functional optimization problems arising in Operations Research are investigated. In such problems, a cost functional ? has to be minimized over an admissible set S of d-variable functions. As, in general, closed-form solutions cannot be derived, suboptimal solutions are searched for, having the form of variable-basis functions, i.e., elements of the set span n   G of linear combinations of at most n elements from a set G of computational units. Upper bounds on inff?S?spannG?(f)?inff?S?(f) are obtained. Conditions are derived, under which the estimates do not exhibit the so-called ?curse of dimensionality? in the number n of computational units, when the number d of variables grows. The problems considered include dynamic optimization, team optimization, and supervised learning from data.
KW  - Optimization
KW  -  Operations Research/Decision Theory
KW  -     Industrial and Production Engineering
UR  - http://dx.doi.org/10.1007/s10288-010-0134-8
A1  - Gnecco, Giorgio
TI  - Functional optimization by variable-basis approximation schemes
Y1  - 2011///
ID  - eprints1705
SP  - 103
SN  - 1619-4500
AV  - none
ER  -