150 citations · 324 across the 5 of their papers we have counts for
5 papers
Randomized Block Coordinate Descent for Online and Stochastic Optimization
Huahua Wang, Arindam Banerjee
Two types of low cost-per-iteration gradient descent methods have been extensively studied in parallel. One is online or stochastic gradient descent (OGD/SGD), and the other is ran…
Parallel Direction Method of Multipliers
Huahua Wang, Arindam Banerjee, Zhi-Quan Luo
We consider the problem of minimizing block-separable convex functions subject to linear constraints. While the Alternating Direction Method of Multipliers (ADMM) for two-block lin…
Bethe-ADMM for Tree Decomposition based Parallel MAP Inference
Qiang Fu, Huahua Wang, Arindam Banerjee
We consider the problem of maximum a posteriori (MAP) inference in discrete graphical models. We present a parallel MAP inference algorithm called Bethe-ADMM based on two ideas: tr…
Bregman Alternating Direction Method of Multipliers
Huahua Wang, Arindam Banerjee
The mirror descent algorithm (MDA) generalizes gradient descent by using a Bregman divergence to replace squared Euclidean distance. In this paper, we similarly generalize the alte…
Online Alternating Direction Method
Huahua Wang, Arindam Banerjee
Online optimization has emerged as powerful tool in large scale optimization. In this paper, we introduce efficient online algorithms based on the alternating directions method (AD…