Multi-agent mirror descent for decentralized stochastic optimization

By Michael G. Rabbat. 2015

48 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Management Science and Operations Research

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Citations in the corpus

A Unified Theory of Decentralized SGD with Changing Topology and Local Updates (2020, methodology): “Rates for the stochastic optimization are derived in (Shamir & Srebro, 2014; Rabbat, 2015), under the assumption that the distributions on all nodes are equal.”
Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication (2019, background): “Rates for the stochastic setting are derived in (Shamir & Srebro, 2014; Rabbat, 2015), under the assumption that the distributions on all nodes are equal.”
Communication-efficient algorithms for decentralized and stochastic optimization (2017, neutral): “all of which require O(1/ǫ2) inter-node communications and intra-node gradient computations to obtain an ǫ-optimal solution. Multiagent mirror descent method for decentralized stochastic optimization [46] showed a O(1/ǫ) complexity bound when the objective functions are strongly convex. An alternati”
Optimal Statistical Rates for Decentralised Non-Parametric Regression with Linear Speed-Up (2019, background): “The authors in [37, 51] investigate a distributed regularised online learning setting [55] and obtain guarantees for a “multi-step” Distributed Stochastic Mirror Descent algorithm where agents reach consensus on their stochastic gradients in-between computation steps.”
Efficient Distributed Online Prediction and Stochastic Optimization With Approximate Distributed Averaging (2014, methodology): “Initial work in this direction suggests that similar improvements are possible in the distributed multiagent setting using primal and dual methods similar to the one described in this paper [18], [26].”
Robust Distributed Accelerated Stochastic Gradient Methods for Multi-Agent Networks (2019, unknown): “Algorithm Extra Assumption αk Convergence Rate D-SG Tsianos and Rabbat (2012) Yes‡ O ( 1 k ) Ef ( x (k) i ) − f∗ ≤ O ( G2 log( √ Nk) (1−√γ)k )”
Distributed Online Optimization in Dynamic Environments Using Mirror Descent (2016, background): “Moreover, Rabbat [23] proposes a decentralized mirror descent for stochastic composite optimization problems and provides guarantees for strongly convex regularizers.”