Multilevel Stochastic Gradient Methods for Nested Composition Optimization
| Autor: | Mengdi Wang, Ethan X. Fang, Shuoguang Yang |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Mathematical optimization
021103 operations research Optimization problem Statistical learning Sample complexity 0211 other engineering and technologies 010103 numerical & computational mathematics 02 engineering and technology Composition (combinatorics) 01 natural sciences Theoretical Computer Science Convex optimization Scalability Stochastic optimization 0101 mathematics Software Mathematics |
| Zdroj: | SIAM Journal on Optimization. 29:616-659 |
| ISSN: | 1095-7189 1052-6234 |
| DOI: | 10.1137/18m1164846 |
| Popis: | Stochastic gradient methods are scalable for solving large-scale optimization problems that involve empirical expectations of loss functions. Existing results mainly apply to optimization problems ... |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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