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SUMMARY:Multivariate polynomial quadrature via feature selection - Departm
 ent of Mathematics\, University of Utah
DTSTART:20180713T160000Z
DTEND:20180713T170000Z
UID:TALK107920@talks.cam.ac.uk
CONTACT:Pranay Seshadri
DESCRIPTION:Numerical quadrature rules that use point values are ubiquitou
 s tools for approximating integrals. Some of the most popular rules achiev
 e accuracy by enforcing exactness for integrands in a finite-dimensional p
 olynomial space. When the integration domain is one-dimensional\, classica
 l rules are available and plentiful. In multidimensional domains with non-
 standard polynomial spaces and weights\, the situation is far more complic
 ated.\n\nWe will present a general methodology for numerically generating 
 approximate polynomial quadrature rules in multidimensional situations. Ou
 r technique is based on recent advances in feature selection algorithms\, 
 allowing rigorous guarantees on feasibility and computability of the selec
 tion problem. We show that this approach allows one to generate excellent 
 quadrature rules in an automated way\, and demonstrate the accuracy of the
 se rules in applications.\n
LOCATION:EDC Loft meeting room (Inglis Building\, CUED)
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