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.
Dadam: A consensus- based distributed adaptive gradient method for online optimization
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New adaptive decentralized algorithms select stepsizes from local curvature estimates derived from a Lyapunov function, delivering sublinear convergence for convex problems and linear rates for strongly convex ones.
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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.
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A Line-search-free Method for Adaptive Decentralized Optimization
New adaptive decentralized algorithms select stepsizes from local curvature estimates derived from a Lyapunov function, delivering sublinear convergence for convex problems and linear rates for strongly convex ones.