A communication-efficient distributed algorithm is proposed for fixed-point seeking of biased stochastic operators using inexact iterations, compression, and period skipping, with convergence shown under relaxed conditions and unified with non-convex optimization.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
math.OC 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A unified compression algorithm for distributed nonconvex optimization achieves O(1/sqrt(T)) convergence for locally-bounded compressors, matching centralized 1-bit methods, with an improved O(1/T^{2/3}) rate after one uncompressed round.
citing papers explorer
-
Distributed Seeking for Fixed Points of Biased Stochastic Operators: A Communication-Efficient Approach
A communication-efficient distributed algorithm is proposed for fixed-point seeking of biased stochastic operators using inexact iterations, compression, and period skipping, with convergence shown under relaxed conditions and unified with non-convex optimization.
-
Unified Compression Algorithm for Distributed Nonconvex Optimization: Generalized to 1-Bit, Saturation, and Bounded Noise
A unified compression algorithm for distributed nonconvex optimization achieves O(1/sqrt(T)) convergence for locally-bounded compressors, matching centralized 1-bit methods, with an improved O(1/T^{2/3}) rate after one uncompressed round.