Randomized optimal stopping algorithms and their convergence analysis
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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.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 3 Feb 2020.
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Citing papers per year: 2022: 1; 2023: 2; 2024: 1; 2025: 1; 2026: 1.
6 papers.
- 2022Deep neural network expressivity for optimal stopping problemscited by 9
- 2024Exploratory Optimal Stopping: A Singular Control Formulationcited by 9
- 2023Simultaneous upper and lower bounds of American-style option prices with hedging via neural networkscited by 3
- 2023Machine learning for option pricing: an empirical investigation of network architecturescited by 3
- 2025Deep Learning for the Multiple Optimal Stopping Problemcited by 1
- 2026Reinforcement Learning in Real Option Modelscited by 0
- 2026Reinforcement Learning in Real Option Models17 Feb 2026
- 2025Deep Learning for the Multiple Optimal Stopping Problem28 Dec 2025
- 2024Exploratory Optimal Stopping: A Singular Control Formulation18 Aug 2024
- 2023Machine learning for option pricing: an empirical investigation of network architectures14 Jul 2023
- 2023Simultaneous upper and lower bounds of American-style option prices with hedging via neural networks24 Feb 2023
- 2022Deep neural network expressivity for optimal stopping problems19 Oct 2022