Local Acceptance without Global Agreement: Convergence of Decentralized Trust-Region Methods

We study decentralized smooth nonconvex optimization with a lower-bounded average objective and independent local trust-region acceptance decisions. Successful local trials can increase the average objective, while rejection produces a zero accepted inverse block at a nonzero gradient tracker. Our main contribution is a convergence analysis that permits such genuine local rejections. The method combines gradient … Read more

Distributed Projections onto a Simplex

Projecting a vector onto a simplex is a well-studied problem that arises in a wide range of optimization problems. Numerous algorithms have been proposed for determining the projection; however, all but one of these algorithms are serial. We address this gap by developing a method that preprocesses the input vector by decomposing and distributing it … Read more

A Class of Randomized Primal-Dual Algorithms for Distributed Optimization

Based on a preconditioned version of the randomized block-coordinate forward-backward algorithm recently proposed in [Combettes,Pesquet,2014], several variants of block-coordinate primal-dual algorithms are designed in order to solve a wide array of monotone inclusion problems. These methods rely on a sweep of blocks of variables which are activated at each iteration according to a random rule, … Read more