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SUMMARY:Stochastic optimization methods for the simultaneous control o
 f parameter-dependent systems (Biccari\, Deusto Spain)
UID:bab3ca4d-dc53-46a9-989d-cc72a340621d
DESCRIPTION:Some recent results about the controllability of the heat 
 equation Speaker: Umberto Biccari Affiliation: Chair of Computational 
 Mathematics\, Fundación Deusto and Universidad de Deusto\, Bilbao\, S
 pain Abstract: We address the application of stochastic optimization m
 ethods for the simultaneous control of parameter-dependent systems. In
  particular\, we focus on the classical Stochastic Gradient Descent (S
 GD) approach of Robbins and Monro\, and on the recently developed Cont
 inuous Stochastic Gradient (CSG) algorithm. We consider the problem of
  computing simultaneous controls through the minimization of a cost fu
 nctional defined as the superposition of individual costs for each rea
 lization of the system. We compare the performances of these stochasti
 c approaches\, in terms of their computational complexity\, with those
  of the more classical Gradient Descent (GD) and Conjugate Gradient (C
 G) algorithms\, and we discuss the advantages and disadvantages of eac
 h methodology. In agreement with well-established results in the machi
 ne learning context\, we show how the SGD and CSG algorithms can signi
 ficantly reduce the computational burden when treating control problem
 s depending on a large number of parameters. This is corroborated by n
 umerical experiments. Applied Analysis
DTSTART:20200612T083500Z
DTEND:20200612T090500Z
LOCATION:Online (contact marius.yamakou@fau to get the data for the VC)
DTSTAMP:20260725T043159Z
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