Extrapolation algorithm for convex feasibility problems with application to Semidefinite Programming
by Associate Professor Serge Kruk
Abstract: The convex feasibility problem under consideration is to find a common point of a countable family of closed affine subspaces and convex sets in a Hilbert space. We describe a general parallel block-iterative algorithmic framework in which the affine
subspaces are exploited to introduce extrapolated over-relaxations.
This framework encompasses a wide range of projection, subgradient projection, proximal, and fixed point methods encountered in various
branches of optimization. Numerical experiments in the context of large scale semidefinite programming are provided to illustrate the
benefits of the extrapolations.
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