# Random Function Iterations for Consistent Stochastic Feasibility

arXiv: https://arxiv.org/abs/1808.05426 · HTML: https://mlsanity.com/paper/1808.05426/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2018.

## Built on by (most cited first, 1 of 1)

- [Unsupervised Ground Metric Learning](https://mlsanity.com/paper/2507.13094/) (2025)

## Abstract (arXiv)

We study the convergence of stochastic fixed point iterations in the consistent case (in the sense of Butnariu and Flaam (1995)) in several different settings, under decreasingly restrictive regularity assumptions of the fixed point mappings. The iterations are Markov chains and, for the purposes of this study, convergence is understood in very restrictive terms. We show that sufficient conditions for geometric (linear) convergence in expectation of stochastic projection algorithms presented in Nedi'c (2011), are in fact necessary for geometric (linear) convergence in expectation more generally of iterated random functions.