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Improved Iteration Complexity Bounds of Cyclic Block Coordinate Descent for Convex Problems
Ruoyu Sun, Mingyi Hong
The iteration complexity of the block-coordinate descent (BCD) type algorithm has been under extensive investigation. It was recently shown that for convex problems the classical c…
A Unified Algorithmic Framework for Block-Structured Optimization Involving Big Data
Mingyi Hong, Meisam Razaviyayn, Zhi-Quan Luo +1
This article presents a powerful algorithmic framework for big data optimization, called the Block Successive Upper bound Minimization (BSUM). The BSUM includes as special cases ma…
Stochastic Proximal Gradient Consensus Over Random Networks
Mingyi Hong, Tsung-Hui Chang
We consider solving a convex, possibly stochastic optimization problem over a randomly time-varying multi-agent network. Each agent has access to some local objective function, and…
Quantized Consensus ADMM for Multi-Agent Distributed Optimization
Shengyu Zhu, Mingyi Hong, Biao Chen
Multi-agent distributed optimization over a network minimizes a global objective formed by a sum of local convex functions using only local computation and communication. We develo…
Asynchronous Distributed ADMM for Large-Scale Optimization- Part II: Linear Convergence Analysis and Numerical Performance
Tsung-Hui Chang, Wei-Cheng Liao, Mingyi Hong +1
The alternating direction method of multipliers (ADMM) has been recognized as a versatile approach for solving modern large-scale machine learning and signal processing problems ef…
Asynchronous Distributed ADMM for Large-Scale Optimization- Part I: Algorithm and Convergence Analysis
Tsung-Hui Chang, Mingyi Hong, Wei-Cheng Liao +1
Aiming at solving large-scale learning problems, this paper studies distributed optimization methods based on the alternating direction method of multipliers (ADMM). By formulating…