261 citations · 503 across the 19 of their papers we have counts for
19 papers
Communication-Efficient Topologies for Decentralized Learning with Consensus Rate
Zhuoqing Song, Weijian Li, Kexin Jin +4
Decentralized optimization is an emerging paradigm in distributed learning in which agents achieve network-wide solutions by peer-to-peer communication without the central server.…
Optimal Gradient Tracking for Decentralized Optimization
Zhuoqing Song, Lei Shi, Shi Pu +1
In this paper, we focus on solving the decentralized optimization problem of minimizing the sum of objective functions over a multi-agent network. The agents are embedded in an…
Decentralized Composite Optimization with Compression
Yao Li, Xiaorui Liu, Jiliang Tang +2
Decentralized optimization and communication compression have exhibited their great potential in accelerating distributed machine learning by mitigating the communication bottlenec…
Provably Accelerated Decentralized Gradient Method Over Unbalanced Directed Graphs
Zhuoqing Song, Lei Shi, Shi Pu +1
We consider the decentralized optimization problem, where a network of agents aims to collaboratively minimize the average of their individual smooth and convex objective funct…
Fast algorithms for robust principal component analysis with an upper bound on the rank
Ningyu Sha, Lei Shi, Ming Yan
The robust principal component analysis (RPCA) decomposes a data matrix into a low-rank part and a sparse part. There are mainly two types of algorithms for RPCA. The first type of…
A Novel Regularization Based on the Error Function for Sparse Recovery
Weihong Guo, Yifei Lou, Jing Qin +1
Regularization plays an important role in solving ill-posed problems by adding extra information about the desired solution, such as sparsity. Many regularization terms usually inv…
Variational Asymptotic Preserving Scheme for the Vlasov-Poisson-Fokker-Planck System
Jose A. Carrillo, Li Wang, Wuzhe Xu +1
We design a variational asymptotic preserving scheme for the Vlasov-Poisson-Fokker-Planck system with the high field scaling, which describes the Brownian motion of a large system…
Linear Convergent Decentralized Optimization with Compression
Xiaorui Liu, Yao Li, Rongrong Wang +2
Communication compression has become a key strategy to speed up distributed optimization. However, existing decentralized algorithms with compression mainly focus on compressing DG…
A Multi-Agent Primal-Dual Strategy for Composite Optimization over Distributed Features
Sulaiman A. Alghunaim, Ming Yan, Ali H. Sayed
This work studies multi-agent sharing optimization problems with the objective function being the sum of smooth local functions plus a convex (possibly non-smooth) function couplin…
Decentralized Frequency Alignment for Collaborative Beamforming in Distributed Phased Arrays
Hassna Ouassal, Ming Yan, Jeffrey A. Nanzer
A new approach to distributed syntonization (frequency alignment) for the coordination of nodes in open loop coherent distributed antenna arrays to enable distributed beamforming i…
Manifold Denoising by Nonlinear Robust Principal Component Analysis
He Lyu, Ningyu Sha, Shuyang Qin +3
This paper extends robust principal component analysis (RPCA) to nonlinear manifolds. Suppose that the observed data matrix is the sum of a sparse component and a component drawn f…
A Double Residual Compression Algorithm for Efficient Distributed Learning
Xiaorui Liu, Yao Li, Jiliang Tang +1
Large-scale machine learning models are often trained by parallel stochastic gradient descent algorithms. However, the communication cost of gradient aggregation and model synchron…
On linear convergence of two decentralized algorithms
Yao Li, Ming Yan
Decentralized algorithms solve multi-agent problems over a connected network, where the information can only be exchanged with the accessible neighbors. Though there exist several…
Accelerated Schemes for the Minimization
Chao Wang, Ming Yan, Yaghoub Rahimi +1
In this paper, we consider the minimization for sparse recovery and study its relationship with the - model. Based on this relationship, we propose three nu…
New convergence analysis of a primal-dual algorithm with large stepsizes
Zhi Li, Ming Yan
We consider a primal-dual algorithm for minimizing with Fréchet differentiable and . This primal-dual algorithm has two names in literature: Primal-D…
A decentralized proximal-gradient method with network independent step-sizes and separated convergence rates
Zhi Li, Wei Shi, Ming Yan
This paper proposes a novel proximal-gradient algorithm for a decentralized optimization problem with a composite objective containing smooth and non-smooth terms. Specifically, th…
On the Convergence of Asynchronous Parallel Iteration with Unbounded Delays
Zhimin Peng, Yangyang Xu, Ming Yan +1
Recent years have witnessed the surge of asynchronous parallel (async-parallel) iterative algorithms due to problems involving very large-scale data and a large number of decision…
Fast Adaptive Algorithm for Robust Evaluation of Quality of Experience
Qianqian Xu, Ming Yan, Yuan Yao
Outlier detection is an integral part of robust evaluation for crowdsourceable Quality of Experience (QoE) and has attracted much attention in recent years. In QoE for multimedia,…
Self Equivalence of the Alternating Direction Method of Multipliers
Ming Yan, Wotao Yin
The alternating direction method of multipliers (ADM or ADMM) breaks a complex optimization problem into much simpler subproblems. The ADM algorithms are typically short and easy t…