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AdGT: Decentralized Gradient Tracking with Adaptive Per-Agent Stepsizes
In decentralized optimization, gradient-tracking methods typically rely on a single global stepsize. This choice can be conservative when agents have local objectives with different smoothness constants, since the stepsize must remain stable for the agent with the largest smoothness constant. This p
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