4 papers · 1 filter
SILAGE: Memory-Efficient, Full-Gradient-Free Nonconvex Optimization for Nested Finite Sums
Igor Sokolov, Laurent Condat, Peter Richtárik
Empirical risk minimization on massive datasets naturally exhibits a nested double finite-sum structure, where total samples are logically or physically partitioned into …
A Unified Primal-Dual Recipe for Accelerating Three-Operator Splitting Methods
Abdurakhmon Sadiev, Laurent Condat, Peter Richtárik
Composite optimization problems, formulated as the minimization of three functions, are ubiquitous in large-scale machine learning and signal processing. While state-of-the-art spl…
A Nesterov-Accelerated Primal-Dual Splitting Algorithm for Convex Nonsmooth Optimization
Laurent Condat, Abdurakhmon Sadiev, Peter Richtárik
We investigate the integration of Nesterov-type acceleration into primal-dual methods for structured convex optimization. While proximal splitting algorithms efficiently handle com…
BiCoLoR: Communication-Efficient Optimization with Bidirectional Compression and Local Training
Laurent Condat, Artavazd Maranjyan, Peter Richtárik
Slow and costly communication is often the main bottleneck in distributed optimization, especially in federated learning where it occurs over wireless networks. We introduce BiCoLo…