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OSGA: a fast subgradient algorithm with optimal complexity.
- Source :
- Mathematical Programming; Jul2016, Vol. 158 Issue 1/2, p1-21, 21p
- Publication Year :
- 2016
-
Abstract
- This paper presents an algorithm for approximately minimizing a convex function in simple, not necessarily bounded convex, finite-dimensional domains, assuming only that function values and subgradients are available. No global information about the objective function is needed apart from a strong convexity parameter (which can be put to zero if only convexity is known). The worst case number of iterations needed to achieve a given accuracy is independent of the dimension and-apart from a constant factor-best possible under a variety of smoothness assumptions on the objective function. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00255610
- Volume :
- 158
- Issue :
- 1/2
- Database :
- Complementary Index
- Journal :
- Mathematical Programming
- Publication Type :
- Academic Journal
- Accession number :
- 116123052
- Full Text :
- https://doi.org/10.1007/s10107-015-0911-4