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    Towards accelerated rates for distributed optimization over time-varying networks

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    Type
    Preprint
    Authors
    Rogozin, Alexander
    Lukoshkin, Vladislav
    Gasnikov, Alexander
    Kovalev, Dmitry
    Shulgin, Egor
    KAUST Department
    Computer Science
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2020-09-23
    Permanent link to this record
    http://hdl.handle.net/10754/665439
    
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    Abstract
    We study the problem of decentralized optimization over time-varying networks with strongly convex smooth cost functions. In our approach, nodes run a multi-step gossip procedure after making each gradient update, thus ensuring approximate consensus at each iteration, while the outer loop is based on accelerated Nesterov scheme. The algorithm achieves precision $\varepsilon > 0$ in $O(\sqrt{\kappa_g}\chi\log^2(1/\varepsilon))$ communication steps and $O(\sqrt{\kappa_g}\log(1/\varepsilon))$ gradient computations at each node, where $\kappa_g$ is the global function number and $\chi$ characterizes connectivity of the communication network. In the case of a static network, $\chi = 1/\gamma$ where $\gamma$ denotes the normalized spectral gap of communication matrix $\mathbf{W}$. The complexity bound includes $\kappa_g$, which can be significantly better than the worst-case condition number among the nodes.
    Publisher
    arXiv
    arXiv
    2009.11069
    Additional Links
    https://arxiv.org/pdf/2009.11069
    Collections
    Preprints; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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