Distributed Stochastic Nonsmooth Nonconvex Optimization

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00360156" target="_blank" >RIV/68407700:21230/22:00360156 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1016/j.orl.2022.09.001" target="_blank" >https://doi.org/10.1016/j.orl.2022.09.001</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.orl.2022.09.001" target="_blank" >10.1016/j.orl.2022.09.001</a>

Result language
angličtina
Original language name
Distributed Stochastic Nonsmooth Nonconvex Optimization
Original language description
Distributed consensus optimization has received considerable attention in recent years and several distributed consensus-based algorithms have been proposed for (nonsmooth) convex and (smooth) nonconvex objective functions. However, the behavior of these distributed algorithms on nonconvex, nonsmooth and stochastic objective functions is not understood. Such class of functions and distributed setting are motivated by several applications, including problems in machine learning and signal processing. This paper presents the first convergence analysis of the decentralized stochastic subgradient method for such classes of problems, over networks modeled as undirected, fixed, graphs.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
<a href="/en/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Research Center for Informatics</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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