Descentwise inexact proximal algorithms for smooth optimization

摘要

The proximal method is a standard regularization approach in optimization. Practical implementations of this algorithm require (i) an algorithm to compute the proximal point, (ii) a rule to stop this algorithm, (iii) an update formula for the proximal parameter. In this work we focus on (ii), when smoothness is present—so that Newton-like methods can be used for (i): we aim at giving adequate stopping rules to reach overall efficiency of the method.