The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
Optimal Stochastic Approximation Algorithms for Strongly Convex Stochastic Composite Optimization I: A Generic Algorithmic Framework
Abstract
In this paper we present a generic algorithmic framework, namely, the accelerated stochastic approximation (AC-SA) algorithm, for solving strongly convex stochastic composite optimization (SCO) problems. While the classical stochastic approximation algorithms are asymptotically optimal for solving differentiable and strongly convex problems, the AC-SA algorithm, when employed with proper stepsize policies, can achieve optimal or nearly optimal rates of convergence for solving different classes of SCO problems during a given number of iterations. Moreover, we investigate these AC-SA algorithms in more detail, such as by establishing the large-deviation results associated with the convergence rates and introducing an efficient validation procedure to check the accuracy of the generated solutions.
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Cited by
A Stochastic Gradient Method with an Exponential Convergence Rate for Finite Training Sets
Roux, N. L., Schmidt, M., & Bach, F. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1202.6258 · Cited by 481
First-order methods of smooth convex optimization with inexact oracle
Devolder, O., Glineur, F., & Nesterov, Y. (2014) · Mathematical Programming · doi:10.1007/s10107-013-0677-5 · Cited by 422
Metadata
- Authors
- Ghadimi, S., & Lan, G.
- Venue
- SIAM Journal on Optimization
- Published
- 2012-01
- Volume
- 22
- Issue
- 4
- Pages
- 1469-1492
- ISSN
- 1052-6234 (ISSN-L)
- 1095-7189
- OpenAlex
- W2045744861
- Cited by
- 269
- References
- 43
- FWCI
- 14.88
- Type
- journal article
- Language
- en
- Open access
- closed
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