mlOSP: Towards a Unified Implementation of Regression Monte Carlo Algorithms. We introduce mlOSP, a computational template for Machine Learning for Optimal Stopping Problems. The template is implemented in the R statistical environment and publicly available via a GitHub repository. mlOSP presents a unified numerical implementation of Regression Monte Carlo (RMC) approaches to optimal stopping, providing a state-of-the-art, open-source, reproducible and transparent platform. Highlighting its modular nature, we present multiple novel variants of RMC algorithms, especially in terms of constructing simulation designs for training the regressors, as well as in terms of machine learning regression modules. At the same time, mlOSP nests most of the existing RMC schemes, allowing for a consistent and verifiable benchmarking of extant algorithms. The article contains extensive R code snippets and figures, and serves the dual role of presenting new RMC features and as a vignette to the underlying software package.
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References in zbMATH (referenced in 1 article , 1 standard article )
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- Mike Ludkovski: mlOSP: Towards a Unified Implementation of Regression Monte Carlo Algorithms (2020) arXiv