33 citations · 92 across the 19 of their papers we have counts for
3 papers · 2 filters
Boosting Data-Driven Mirror Descent with Randomization, Equivariance, and Acceleration
Hong Ye Tan, Subhadip Mukherjee, Junqi Tang +1
Learning-to-optimize (L2O) is an emerging research area in large-scale optimization with applications in data science. Recently, researchers have proposed a novel L2O framework cal…
Provably Convergent Plug-and-Play Quasi-Newton Methods
Hong Ye Tan, Subhadip Mukherjee, Junqi Tang +1
Plug-and-Play (PnP) methods are a class of efficient iterative methods that aim to combine data fidelity terms and deep denoisers using classical optimization algorithms, such as I…
Stochastic Primal Dual Hybrid Gradient Algorithm with Adaptive Step-Sizes
Antonin Chambolle, Claire Delplancke, Matthias J. Ehrhardt +2
In this work we propose a new primal-dual algorithm with adaptive step-sizes. The stochastic primal-dual hybrid gradient (SPDHG) algorithm with constant step-sizes has become widel…