seminaire: G. Patrizi (LIX, Ecole Polytechnique, 5/12/07 at 10:30)
Forum 'Annonces' - Sujet créé le 2007-11-03 par Leo Liberti
Seminar at LIX (Salle des seminaires) 5/12/2007, 10:30.
Giacomo Patrizi, Laura Di Giacomo
DSPSA, La Sapienza, Roma
Title: Certainty equivalent predictions for dynamic systems through
multivalued non linear variational inequalities
An empirical process is a data set, through which it is desired to
formulate a model of the operation of the underlying phenomenon, so
that suitable controls to achieve determinate objectives may be
defined and applied. The aim of this paper is to formulate an
algorithm based on generalized variational inequalities with setvalued
maps to determine at the same time the optimal model of the process
and the optimal control, to be applied, which must be certainty
equivalent to the ex post facto optimal control of the process. This
formulation will be contrastred to the well known robust optimization
approach. The problem so defined can be formulated as a generalized
variational inequality problem and it will be shown how this
formulation generalizes classical variational problems. Uncertainty
ects, often defined as additive components in the traditional
approach, may be an essential part of the nonlinear empirical process
specified and thus cannot be excluded.
Giacomo Patrizi, Laura Di Giacomo
DSPSA, La Sapienza, Roma
Title: Certainty equivalent predictions for dynamic systems through
multivalued non linear variational inequalities
An empirical process is a data set, through which it is desired to
formulate a model of the operation of the underlying phenomenon, so
that suitable controls to achieve determinate objectives may be
defined and applied. The aim of this paper is to formulate an
algorithm based on generalized variational inequalities with setvalued
maps to determine at the same time the optimal model of the process
and the optimal control, to be applied, which must be certainty
equivalent to the ex post facto optimal control of the process. This
formulation will be contrastred to the well known robust optimization
approach. The problem so defined can be formulated as a generalized
variational inequality problem and it will be shown how this
formulation generalizes classical variational problems. Uncertainty
ects, often defined as additive components in the traditional
approach, may be an essential part of the nonlinear empirical process
specified and thus cannot be excluded.