Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
173 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Adaptation to Inexactness for some Gradient-type Methods (2110.05117v1)

Published 11 Oct 2021 in math.OC

Abstract: We introduce a notion of inexact model of a convex objective function, which allows for errors both in the function and in its gradient. For this situation, a gradient method with an adaptive adjustment of some parameters of the model is proposed and an estimate for the convergence rate is found. This estimate is optimal on a class of sufficiently smooth problems in the presence of errors. We consider a special class of convex nonsmooth optimization problems. In order to apply the proposed technique to this class, an artificial error should be introduced. We show that the method can be modified for such problems to guarantee a convergence in the function with a nearly optimal rate on the class of convex nonsmooth optimization problems. An adaptive gradient method is proposed for objective functions with some relaxation of the Lipschitz condition for the gradient that satisfy the Polyak--Lojasievicz gradient dominance condition. Here, the objective function and its gradient can be given inexactly. The adaptive choice of the parameters is performed during the operation of the method with respect to both the Lipschitz constant of the gradient and a value corresponding to the error of the gradient and the objective function. The linear convergence of the method is justified up to a value associated with the errors.

Summary

We haven't generated a summary for this paper yet.