9 citations · 15 across the 4 of their papers we have counts for
11 papers
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…
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…