3 citations · 4 across the 11 of their papers we have counts for
5 papers · 1 filter
Strong Convergence of FISTA for Affinely Constrained Convex Quadratic Minimization
Sedi Bartz, Heinz H. Bauschke, Yuan Gao +1
In October 2025, research by Boţ, Fadili, and Nguyen, and by Jang and Ryu, led to the seminal result that Beck and Teboulle's FISTA converges weakly to a minimizer of the sum of tw…
Composite Optimization with Error Feedback: the Dual Averaging Approach
Yuan Gao, Anton Rodomanov, Jeremy Rack +1
Communication efficiency is a central challenge in distributed machine learning training, and message compression is a widely used solution. However, standard Error Feedback (EF) m…
Accelerated Distributed Optimization with Compression and Error Feedback
Yuan Gao, Anton Rodomanov, Jeremy Rack +1
Modern machine learning tasks often involve massive datasets and models, necessitating distributed optimization algorithms with reduced communication overhead. Communication compre…
Non-convex Stochastic Composite Optimization with Polyak Momentum
Yuan Gao, Anton Rodomanov, Sebastian U. Stich
The stochastic proximal gradient method is a powerful generalization of the widely used stochastic gradient descent (SGD) method and has found numerous applications in Machine Lear…
EControl: Fast Distributed Optimization with Compression and Error Control
Yuan Gao, Rustem Islamov, Sebastian Stich
Modern distributed training relies heavily on communication compression to reduce the communication overhead. In this work, we study algorithms employing a popular class of contrac…