Gnecco, Giorgio Functional Optimization by VariableBasis Approximation Schemes. 4OR: A Quarterly Journal of Operations Research, 9 (1). pp. 103106. ISSN 16194500 (2011)
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Abstract
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 Φ has to be minimized over an admissible set S of dvariable functions. As, in general, closedform solutions cannot be derived, suboptimal solutions are searched for, having the form of variablebasis 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∩spannGΦ(f)−inff∈SΦ(f) are obtained. Conditions are derived, under which the estimates do not exhibit the socalled “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.
Item Type:  Article 

Identification Number:  10.1007/s1028801001348 
Uncontrolled Keywords:  Optimization, Operations Research/Decision Theory, Industrial and Production Engineering 
Subjects:  Q Science > QA Mathematics > QA75 Electronic computers. Computer science 
Research Area:  Computer Science and Applications 
Depositing User:  Giorgio Gnecco 
Date Deposited:  17 Sep 2013 13:05 
Last Modified:  17 Sep 2013 13:05 
URI:  http://eprints.imtlucca.it/id/eprint/1766 
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Functional optimization by variablebasis approximation schemes. (deposited 12 Sep 2013 13:38)
 Functional Optimization by VariableBasis Approximation Schemes. (deposited 17 Sep 2013 13:05) [Currently Displayed]
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