An incremental mirror descent subgradient algorithm with random sweeping and proximal step

We investigate the convergence properties of incremental mirror descent type subgradient algorithms for minimizing the sum of convex functions. In each step, we only evaluate the subgradient of a single component function and mirror it back to the feasible domain, which makes iterations very cheap t...

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Detalles Bibliográficos
Autores principales: Boţ, Radu Ioan, Böhm, Axel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Taylor & Francis 2018
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6382287/
https://www.ncbi.nlm.nih.gov/pubmed/30828224
http://dx.doi.org/10.1080/02331934.2018.1482491

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6382287/
https://www.ncbi.nlm.nih.gov/pubmed/30828224
http://dx.doi.org/10.1080/02331934.2018.1482491

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