Wednesday, May 12

Symbolic and Numerical Computations for Convex Optimization

9:00 AM-10:30 AM
Room: Savannah 3

Convex optimization is a fundamental branch of optimization. It provides a suitable framework for many problems, classical (such as image reconstruction) and contemporary (such as semidefinite programming). In this minisymposium, the speakers will discuss convex optimization problems and associated objects from a computational point of view. They will focus on the symbolic and numerical computation of Fenchel conjugates; the symbolic computation of derivatives of functions on spaces of symmetric matrices; and Fenchel-duality based computational approach to image reconstruction.

Organizers: Heinz H Bauschke
Okanagan University College, Kelowna, Canada
Yves Lucet
Simon Fraser University, Burnaby, Canada

9:00-9:25 Symbolic Derivatives of Matrix Functions
Serge Kruk, St. Jerome's University, and University of Waterloo, Canada
9:30-9:55 Fast Fenchel Transform and Fast Moreau-Yosida Approximate
Yves Lucet, Organizer
New10:00-10:25 Symbolic Fenchel Conjugate in Maple
Heinz H Bauschke, Organizer; and Martin von Mohrenschildt, McMaster University
CANCELLED 10:30-10:55 Minimization of Convex Integral Functionals Under Linear Constraints and pplications
Pierre Marechal, Simon Fraser University, Canada

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