# Randomized optimal stopping algorithms and their convergence analysis

arXiv: https://arxiv.org/abs/2002.00816 · HTML: https://mlsanity.com/paper/2002.00816/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2020.

## Built on by (most cited first, 6 of 6)

- [Deep neural network expressivity for optimal stopping problems](https://mlsanity.com/paper/2210.10443/) (2022)
- [Exploratory Optimal Stopping: A Singular Control Formulation](https://mlsanity.com/paper/2408.09335/) (2024)
- [Simultaneous upper and lower bounds of American-style option prices with hedging via neural networks](https://mlsanity.com/paper/2302.12439/) (2023)
- [Machine learning for option pricing: an empirical investigation of network architectures](https://mlsanity.com/paper/2307.07657/) (2023)
- [Deep Learning for the Multiple Optimal Stopping Problem](https://mlsanity.com/paper/2512.22961/) (2025)
- [Reinforcement Learning in Real Option Models](https://mlsanity.com/paper/2602.15643/) (2026)

## Abstract (arXiv)

In this paper we study randomized optimal stopping problems and consider corresponding forward and backward Monte Carlo based optimisation algorithms. In particular we prove the convergence of the proposed algorithms and derive the corresponding convergence rates.