Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5128333 | Operations Research Letters | 2017 | 4 Pages |
Abstract
This paper gives a unified and succinct approach to the O(1âk),O(1âk), and O(1âk2) convergence rates of the subgradient, gradient, and accelerated gradient methods for unconstrained convex minimization. In the three cases the proof of convergence follows from a generic bound defined by the convex conjugate of the objective function.
Keywords
Related Topics
Physical Sciences and Engineering
Mathematics
Discrete Mathematics and Combinatorics
Authors
Javier Peña,