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Accelerated Consensus and Decentralized Optimization over Slowly Varying Networks
We study average consensus over slowly time-varying networks and its application to decentralized optimization. We introduce WAVE, a windowed Chebyshev method that limits the accumulated effect of network variation by restarting the recurrence after finite windows. If $χ$ bounds the network conditio
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