# Convergence Analysis of Inexact Randomized Iterative Methods

## TL;DR

This paper analyzes the convergence rates of inexact randomized iterative methods, extending classical algorithms by allowing inexact sub-problem solutions and providing complexity results and numerical validation.

## Contribution

It introduces and analyzes inexact variants of several randomized methods, broadening their applicability and understanding of convergence behavior.

## Key findings

- Inexact methods converge under various assumptions on inexactness.
- Numerical experiments show inexactness can improve computational efficiency.
- Many popular randomized algorithms are special cases of the proposed framework.

## Abstract

In this paper we present a convergence rate analysis of inexact variants of several randomized iterative methods. Among the methods studied are: stochastic gradient descent, stochastic Newton, stochastic proximal point and stochastic subspace ascent. A common feature of these methods is that in their update rule a certain sub-problem needs to be solved exactly. We relax this requirement by allowing for the sub-problem to be solved inexactly. In particular, we propose and analyze inexact randomized iterative methods for solving three closely related problems: a convex stochastic quadratic optimization problem, a best approximation problem and its dual, a concave quadratic maximization problem. We provide iteration complexity results under several assumptions on the inexactness error. Inexact variants of many popular and some more exotic methods, including randomized block Kaczmarz, randomized Gaussian Kaczmarz and randomized block coordinate descent, can be cast as special cases. Numerical experiments demonstrate the benefits of allowing inexactness.