101 citations · 139 across the 7 of their papers we have counts for
5 papers · 1 filter
Tight Coefficients of Averaged Operators via Scaled Relative Graph
Xinmeng Huang, Ernest K. Ryu, Wotao Yin
Many iterative methods in optimization are fixed-point iterations with averaged operators. As such methods converge at an rate with the constant determined by th…
Finding the forward-Douglas-Rachford-forward method
Ernest K. Ryu, Bang Cong Vu
We consider the monotone inclusion problem with a sum of 3 operators, in which 2 are monotone and 1 is monotone-Lipschitz. The classical Douglas--Rachford and Forward-backward-forw…
Decentralized Proximal Gradient Algorithms with Linear Convergence Rates
Sulaiman A. Alghunaim, Ernest K. Ryu, Kun Yuan +1
This work studies a class of non-smooth decentralized multi-agent optimization problems where the agents aim at minimizing a sum of local strongly-convex smooth components plus a c…
Plug-and-Play Methods Provably Converge with Properly Trained Denoisers
Ernest K. Ryu, Jialin Liu, Sicheng Wang +3
Plug-and-play (PnP) is a non-convex framework that integrates modern denoising priors, such as BM3D or deep learning-based denoisers, into ADMM or other proximal algorithms. An adv…
ODE Analysis of Stochastic Gradient Methods with Optimism and Anchoring for Minimax Problems
Ernest K. Ryu, Kun Yuan, Wotao Yin
Despite remarkable empirical success, the training dynamics of generative adversarial networks (GAN), which involves solving a minimax game using stochastic gradients, is still poo…