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math.OC2025
Graph-Aware Learning Rates for Decentralized Optimization
Aaron Fainman, Stefan Vlaski
We propose an adaptive step-size rule for decentralized optimization. Choosing a step-size that balances convergence and stability is challenging. This is amplified in the decentra…
math.OC2025
On the Convergence of Decentralized Stochastic Gradient-Tracking with Finite-Time Consensus
Aaron Fainman, Stefan Vlaski
Algorithms for decentralized optimization and learning rely on local optimization steps coupled with combination steps over a graph. Recent works have demonstrated that using a tim…