LADMM

Linearized Alternating Direction Method with Parallel Splitting and Adaptive Penalty for Separable Convex Programs in Machine Learning

We propose linearized alternating direction method with parallel splitting and adaptive penalty for efficiently solving linearly constrained multi-variable separable convex programs, which are abundant in machine learning.