Suboptimal solutions to team optimization problems with stochastic information structure (Articolo in rivista)

Type
Label
  • Suboptimal solutions to team optimization problems with stochastic information structure (Articolo in rivista) (literal)
Anno
  • 2012-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1137/100803481 (literal)
Alternative label
  • G. Gnecco; M. Sanguineti; M. Gaggero (2012)
    Suboptimal solutions to team optimization problems with stochastic information structure
    in SIAM journal on optimization (Print); SIAM Publications, Philadelphia (Stati Uniti d'America)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • G. Gnecco; M. Sanguineti; M. Gaggero (literal)
Pagina inizio
  • 212 (literal)
Pagina fine
  • 243 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
  • Journal Q1 in Theoretical Computer Science (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 22 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
  • 32 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • 1 (literal)
Note
  • ISI Web of Science (WOS) (literal)
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • 1. Department of Communications, Computer, and System Sciences (DIST), University of Genoa, Via Opera Pia 13, 16145 Genova, Italy 2. Institute of Intelligent Systems for Automation (ISSIA), National Research Council of Italy, Via De Marini 6, 16149 Genova, Italy 3. Department of Communications, Computer, and System Sciences (DIST), University of Genoa, Via Opera Pia 13, 16145 Genova, Italy (literal)
Titolo
  • Suboptimal solutions to team optimization problems with stochastic information structure (literal)
Abstract
  • Existence, uniqueness, and approximations of smooth solutions to team optimization problems with stochastic information structure are investigated. Suboptimal strategies made up of linear combinations of basis functions containing adjustable parameters are considered. Estimates of their accuracies are derived by combining properties of the unknown optimal strategies with tools from nonlinear approximation theory. The estimates are obtained for basis functions corresponding to sinusoids with variable frequencies and phases, Gaussians with variable centers and widths, and sigmoidal ridge functions. The theoretical results are applied to a problem of optimal production in a multidivisional firm, for which numerical simulations are presented. (literal)
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