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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...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Taylor & Francis
2018
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| 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 |