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math.OC2023
Decentralized gradient descent maximization method for composite nonconvex strongly-concave minimax problems
Yangyang Xu
Minimax problems have recently attracted a lot of research interests. A few efforts have been made to solve decentralized nonconvex strongly-concave (NCSC) minimax-structured optim…
math.OC2023
Compressed Decentralized Proximal Stochastic Gradient Method for Nonconvex Composite Problems with Heterogeneous Data
Yonggui Yan, Jie Chen, Pin-Yu Chen +3
We first propose a decentralized proximal stochastic gradient tracking method (DProxSGT) for nonconvex stochastic composite problems, with data heterogeneously distributed on multi…
math.OC2021
Zeroth-order Optimization for Composite Problems with Functional Constraints
Zichong Li, Pin-Yu Chen, Sijia Liu +2
In many real-world problems, first-order (FO) derivative evaluations are too expensive or even inaccessible. For solving these problems, zeroth-order (ZO) methods that only need fu…