105 citations · 372 across the 39 of their papers we have counts for
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math.OC2020★ 2 cited
Toward Model Parallelism for Deep Neural Network based on Gradient-free ADMM Framework
Junxiang Wang, Zheng Chai, Yue Cheng +1
Alternating Direction Method of Multipliers (ADMM) has recently been proposed as a potential alternative optimizer to the Stochastic Gradient Descent(SGD) for deep learning problem…
math.OC2018★ 1 cited
Accelerated Gradient-free Neural Network Training by Multi-convex Alternating Optimization
Junxiang Wang, Hongyi Li, Liang Zhao
In recent years, even though Stochastic Gradient Descent (SGD) and its variants are well-known for training neural networks, it suffers from limitations such as the lack of theoret…
math.OC2017★ 2 cited
Nonconvex Generalization of Alternating Direction Method of Multipliers for Nonlinear Equality Constrained Problems
Junxiang Wang, Liang Zhao
The classic Alternating Direction Method of Multipliers (ADMM) is a popular framework to solve linear-equality constrained problems. In this paper, we extend the ADMM naturally to…