# Sharp convergence rates for averaged nonexpansive maps

arXiv: https://arxiv.org/abs/1606.05300 · HTML: https://mlsanity.com/paper/1606.05300/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2016.

## Built on by (most cited first, 2 of 2)

- [Stochastic Halpern iteration in normed spaces and applications to reinforcement learning](https://mlsanity.com/paper/2403.12338/) (2024)
- [Stochastic Inertial Krasnosel'skii-Mann Iteration Achieves Near-Optimal Sample Complexity](https://mlsanity.com/paper/2609.28543/) (2026)

## Abstract (arXiv)

We establish sharp estimates for the convergence rate of the Kranosel'skivi-Mann fixed point iteration in general normed spaces, and we use them to show that the asymptotic regularity bound recently proved in [11] (Israel Journal of Mathematics 199(2), 757-772, 2014) with constant 1/√π is sharp and cannot be improved. To this end we consider the recursive bounds introduced in [3] (Proceedings of the 2nd International Conference on Fixed Point Theory and Applications, World Scientific Press, London, 27-66, 1992) which we reinterpret in terms of a nested family of optimal transport problems. We show that these bounds are tight by building a nonexpansive map T:[0,1]mathbb N→[0,1]mathbb N that attains them with equality, settling the main conjecture in [3]. The recursive bounds are in turn reinterpreted as absorption probabilities for an underlying Markov chain which is used to establish the tightness of the constant 1/√π.