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Near-Optimal Decentralized Momentum Method for Nonconvex-PL Minimax Problems
Feihu Huang, Songcan Chen
Minimax optimization plays an important role in many machine learning tasks such as generative adversarial networks (GANs) and adversarial training. Although recently a wide variet…
Faster Stochastic Alternating Direction Method of Multipliers for Nonconvex Optimization
Feihu Huang, Songcan Chen, Heng Huang
In this paper, we propose a faster stochastic alternating direction method of multipliers (ADMM) for nonconvex optimization by using a new stochastic path-integrated differential e…
Accelerated Stochastic Gradient-free and Projection-free Methods
Feihu Huang, Lue Tao, Songcan Chen
In the paper, we propose a class of accelerated stochastic gradient-free and projection-free (a.k.a., zeroth-order Frank-Wolfe) methods to solve the constrained stochastic and fini…
Zeroth-Order Stochastic Alternating Direction Method of Multipliers for Nonconvex Nonsmooth Optimization
Feihu Huang, Shangqian Gao, Songcan Chen +1
Alternating direction method of multipliers (ADMM) is a popular optimization tool for the composite and constrained problems in machine learning. However, in many machine learning…
Faster Gradient-Free Proximal Stochastic Methods for Nonconvex Nonsmooth Optimization
Feihu Huang, Bin Gu, Zhouyuan Huo +2
Proximal gradient method has been playing an important role to solve many machine learning tasks, especially for the nonsmooth problems. However, in some machine learning problems…
Mini-Batch Stochastic ADMMs for Nonconvex Nonsmooth Optimization
Feihu Huang, Songcan Chen
With the large rising of complex data, the nonconvex models such as nonconvex loss function and nonconvex regularizer are widely used in machine learning and pattern recognition. I…
Linear Convergence of Accelerated Stochastic Gradient Descent for Nonconvex Nonsmooth Optimization
Feihu Huang, Songcan Chen
In this paper, we study the stochastic gradient descent (SGD) method for the nonconvex nonsmooth optimization, and propose an accelerated SGD method by combining the variance reduc…
Stochastic Alternating Direction Method of Multipliers with Variance Reduction for Nonconvex Optimization
Feihu Huang, Songcan Chen, Zhaosong Lu
In the paper, we study the stochastic alternating direction method of multipliers (ADMM) for the nonconvex optimizations, and propose three classes of the nonconvex stochastic ADMM…