Linear convergence rates for extrapolated fixed point algorithms
Sign Up to like & getrecommendations! 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
Sign Up to like & getrecommendations! 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
Communication-Efficient and Differentially-Private Distributed Nash Equilibrium Seeking With Linear Convergence
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Control Systems Letters"
DOI: 10.1109/lcsys.2024.3410634
Abstract: The distributed computation of a Nash equilibrium (NE) for non-cooperative games is gaining increased attention recently. Due to the nature of distributed systems, privacy and communication efficiency are two critical concerns. Traditional approaches often address… read more here.
Keywords: communication efficiency; nash equilibrium; linear convergence;
Multiagent Fully Decentralized Value Function Learning With Linear Convergence Rates
Sign Up to like & getrecommendations! 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
Sign Up to like & getrecommendations! 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 Linear Convergence for Stackelberg Aggregative Games
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Automatic Control"
DOI: 10.1109/tac.2025.3587148
Abstract: This article proposes a hierarchical leader–follower game, called Stackelberg aggregative games, with the consideration of an aggregative variable determined by all the players over a multiagent network. In this problem, a leader makes its decision… read more here.
Keywords: aggregative games; stackelberg aggregative; strategy; leader ... See more keywords
Achieving Linear Convergence in Distributed Aggregative Optimization Over Directed Graphs
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Transactions on Systems, Man, and Cybernetics: Systems"
DOI: 10.1109/tsmc.2024.3382173
Abstract: Distributed aggregative optimization (DAO) is a special class of optimization problems of networking agents where the local objective function of each agent relies on the aggregation of other agents’ decisions as well as its own.… read more here.
Keywords: distributed aggregative; directed graphs; linear convergence; aggregative optimization ... See more keywords
On Global Linear Convergence in Stochastic Nonconvex Optimization for Semidefinite Programming
Sign Up to like & getrecommendations! 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
DINE: Decentralized Inexact Newton With Exact Linear Convergence Rate
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Transactions on Signal Processing"
DOI: 10.1109/tsp.2023.3336175
Abstract: Decentralized learning has recently attracted much research attention because of its robustness and user privacy advantages. Decentralized algorithms play central roles in training machine learning models in decentralized learning. Due to the slow convergence of… read more here.
Keywords: inexact newton; convergence rate; monospace; convergence ... See more keywords
Algorithm and linear convergence of the H-spectral radius of weakly irreducible quasi-positive tensors
Sign Up to like & getrecommendations! Published in 2025 at "Open Mathematics"
DOI: 10.1515/math-2025-0194
Abstract: Abstract A class of weakly irreducible quasi-positive tensors is defined by using directed hypergraphs of tensors, which generalizes the essential positive tensors, weakly positive tensors, generalized weakly positive tensors, and weakly essential irreducible nonnegative tensors.… read more here.
Keywords: weakly irreducible; irreducible quasi; quasi positive; linear convergence ... See more keywords