eprintid: 1705 rev_number: 6 eprint_status: archive userid: 46 dir: disk0/00/00/17/05 datestamp: 2013-09-12 13:38:20 lastmod: 2013-09-16 12:03:00 status_changed: 2013-09-12 13:38:20 type: article metadata_visibility: no_search creators_name: Gnecco, Giorgio creators_id: giorgio.gnecco@imtlucca.it title: Functional optimization by variable-basis approximation schemes ispublished: pub subjects: QA75 divisions: CSA full_text_status: none keywords: Optimization, Operations Research/Decision Theory, Industrial and Production Engineering 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 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∩spannGΦ(f)−inff∈SΦ(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. date: 2011 date_type: published publication: 4OR: A Quarterly Journal of Operations Research volume: 9 number: 1 publisher: Springer pagerange: 103-106 id_number: 10.1007/s10288-010-0134-8 refereed: TRUE issn: 1619-4500 official_url: http://dx.doi.org/10.1007/s10288-010-0134-8 citation: Gnecco, Giorgio Functional optimization by variable-basis approximation schemes. 4OR: A Quarterly Journal of Operations Research, 9 (1). pp. 103-106. ISSN 1619-4500 (2011)