Direct Optimization Using Gaussian Quadrature and Continuous Runge-Kutta Methods: Application to an Innovation Diffusion Model (Articolo in rivista)

Type
Label
  • Direct Optimization Using Gaussian Quadrature and Continuous Runge-Kutta Methods: Application to an Innovation Diffusion Model (Articolo in rivista) (literal)
Anno
  • 2004-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1007/b98005 (literal)
Alternative label
  • Diele F., Marangi C., Ragni S. (2004)
    Direct Optimization Using Gaussian Quadrature and Continuous Runge-Kutta Methods: Application to an Innovation Diffusion Model
    in Lecture notes in computer science; Springer, Berlin (Germania)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Diele F., Marangi C., Ragni S. (literal)
Pagina inizio
  • 426 (literal)
Pagina fine
  • 433 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://www.springerlink.com/content/q7yhjkbc2hdr21c9/fulltext.pdf (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 3039 (literal)
Rivista
Note
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Fasma Diele, Istituto per le Applicazioni del Calcolo M. Picone,CNR, Via Amendola 122, 70126 Bari, Italy; Carmela Marangi, Istituto per le Applicazioni del Calcolo M. Picone,CNR, Via Amendola 122, 70126 Bari, Italy; Stefania Ragni, Facolt√† di Economia, Universit√† di Bari, Via Camillo Rosalba 56, 70100 Bari, Italy (literal)
Titolo
  • Direct Optimization Using Gaussian Quadrature and Continuous Runge-Kutta Methods: Application to an Innovation Diffusion Model (literal)
Abstract
  • In the present paper the discretization of a particular model arising in the economic field of innovation diffusion is developed. It consists of an optimal control problem governed by an ordinary differential equation. We propose a direct optimization approach characterized by an explicit, fixed step-size continuous Runge-Kutta integration for the state variable approximation. Moreover, high-order Gaussian quadrature rules are used to discretize the objective function. In this way, the optimal control problem is converted into a nonlinear programming one which is solved by means of classical algorithms. (literal)
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