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Linear convergence rates for extrapolated fixed point algorithms

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0 Published in 2018 at "Optimization"

DOI: 10.1080/02331934.2018.1512109

Abstract: ABSTRACT We establish linear convergence rates for a certain class of extrapolated fixed point algorithms which are based on dynamic string-averaging methods in a real Hilbert space. This applies, in particular, to the extrapolated simultaneous… read more here.

Keywords: extrapolated fixed; linear convergence; convergence rates; point algorithms ... See more keywords

Linear convergence of gradient projection algorithm for split equality problems

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0 Published in 2018 at "Optimization"

DOI: 10.1080/02331934.2018.1545124

Abstract: ABSTRACT In this paper, we consider the varying stepsize gradient projection algorithm (GPA) for solving the split equality problem (SEP) in Hilbert spaces, and study its linear convergence. In particular, we introduce a notion of… read more here.

Keywords: linear convergence; projection algorithm; gradient projection; split equality ... See more keywords

Multiagent Fully Decentralized Value Function Learning With Linear Convergence Rates

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0 Published in 2021 at "IEEE Transactions on Automatic Control"

DOI: 10.1109/tac.2020.2995814

Abstract: This article develops a fully decentralized multiagent algorithm for policy evaluation. The proposed scheme can be applied to two distinct scenarios. In the first scenario, a collection of agents have distinct datasets gathered by following… read more here.

Keywords: fully decentralized; linear convergence; policy; function ... See more keywords

New Results on the Local Linear Convergence of ADMM: A Joint Approach

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1 Published in 2021 at "IEEE Transactions on Automatic Control"

DOI: 10.1109/tac.2020.3033512

Abstract: Thanks to its versatility, its simplicity, and its fast convergence, alternating direction method of multipliers (ADMM) is among the most widely used approaches for solving a convex problem in distributed form. However, making it running… read more here.

Keywords: linear convergence; convergence admm; local linear; new results ... See more keywords

On Global Linear Convergence in Stochastic Nonconvex Optimization for Semidefinite Programming

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0 Published in 2019 at "IEEE Transactions on Signal Processing"

DOI: 10.1109/tsp.2019.2925609

Abstract: Nonconvex reformulations via low-rank factorization for stochastic convex semidefinite optimization problem have attracted arising attention due to their empirical efficiency and scalability. Compared with the original convex formulations, the nonconvex ones typically involve much fewer… read more here.

Keywords: global linear; optimization; linear convergence; convergence stochastic ... See more keywords