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Conference Papers Year : 2019

Accelerated decentralized optimization with local updates for smooth and strongly convex objectives

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Abstract

In this paper, we study the problem of minimizing a sum of smooth and strongly convex functions split over the nodes of a network in a decentralized fashion. We propose the algorithm ESDACD, a decentralized accelerated algorithm that only requires local synchrony. Its rate depends on the condition number κ of the local functions as well as the network topol-ogy and delays. Under mild assumptions on the topology of the graph, ESDACD takes a time O((τmax + ∆max) κ/γ ln(−1)) to reach a precision where γ is the spectral gap of the graph, τmax the maximum communication delay and ∆max the maximum computation time. Therefore, it matches the rate of SSDA (Scaman et al., 2017), which is optimal when τmax = Ω (∆max). Applying ESDACD to quadratic local functions leads to an accelerated randomized gossip algorithm of rate O(θ gossip /n) where θ gossip is the rate of the standard randomized gossip (Boyd et al., 2006). To the best of our knowledge, it is the first asynchronous gossip algorithm with a provably improved rate of convergence of the second moment of the error. We illustrate these results with experiments in idealized settings.
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Dates and versions

hal-01893568 , version 1 (11-10-2018)
hal-01893568 , version 2 (16-01-2020)

Identifiers

  • HAL Id : hal-01893568 , version 2

Cite

Hadrien Hendrikx, Francis Bach, Laurent Massoulié. Accelerated decentralized optimization with local updates for smooth and strongly convex objectives. AISTATS 2019 - 22nd International Conference on Artificial Intelligence and Statistics, Apr 2019, Naha, Okinawa, Japan. ⟨hal-01893568v2⟩
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