Article ID Journal Published Year Pages File Type
5128333 Operations Research Letters 2017 4 Pages PDF
Abstract

This paper gives a unified and succinct approach to the O(1∕k),O(1∕k), and O(1∕k2) 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.

Related Topics
Physical Sciences and Engineering Mathematics Discrete Mathematics and Combinatorics
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