9 citations · 14 across the 2 of their papers we have counts for
3 papers
math.OC2020★ 9 cited
Randomized Bregman Coordinate Descent Methods for Non-Lipschitz Optimization
Tianxiang Gao, Songtao Lu, Jia Liu +1
We propose a new \textit{randomized Bregman (block) coordinate descent} (RBCD) method for minimizing a composite problem, where the objective function could be either convex or non…
math.OC2019★ 5 cited
Leveraging Two Reference Functions in Block Bregman Proximal Gradient Descent for Non-convex and Non-Lipschitz Problems
Tianxiang Gao, Songtao Lu, Jia Liu +1
In the applications of signal processing and data analytics, there is a wide class of non-convex problems whose objective function is freed from the common global Lipschitz continu…
cs.LG2018
DID: Distributed Incremental Block Coordinate Descent for Nonnegative Matrix Factorization
Tianxiang Gao, Chris Chu
Nonnegative matrix factorization (NMF) has attracted much attention in the last decade as a dimension reduction method in many applications. Due to the explosion in the size of dat…