Dual Randomized Coordinate Descent Method for Solving a Class of Nonconvex Problems

Abstract: We consider a nonconvex optimization problem consisting of maximizing the difference of two convex functions. We present a randomized method that requires low computational effort at each iteration. The described method is a randomized coordinate descent method employed on the so-called Toland-dual problem. We prove subsequence convergence to dual stationarity points, a new notion that we introduce and shown to be tighter than the standard criticality. Almost sure rate of convergence of an optimality measure of the dual sequence is proven. We demonstrate the potential of our results on three Principal Component Analysis (PCA) models resulting in extremely simple algorithms Joint work Marc Teboulle

optimization and control

Audience: researchers in the discipline

Comments: the address and password of the zoom room of the seminar are sent by e-mail on the mailinglist of the seminar one day before each talk

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One World Optimization seminar

Series comments: Description: Online seminar on optimization and related areas

The address and password of the zoom room of the seminar are sent by e-mail on the mailinglist of the seminar one day before each talk

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