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This research addresses the challenges of the optimal stopping problem in high-dimensional settings.

August 9, 2026Stochastic SystemsOpen Access

Polynomial-Time Algorithm for Optimal Stopping with Fixed Accuracy

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Randomized trial derives a polynomial-time algorithm for optimal stopping, suggesting practical improvements for high-dimensional problems.

This research addresses the challenges of the optimal stopping problem in high-dimensional settings.
Developed a novel expansion representation for the optimal stopping value.
Proved that truncating this expansion yields a simulation-based algorithm.
Analyzed the connections to martingale duality theory.
The new algorithm has polynomial computational complexity in the time horizon and underlying dimension for fixed accuracy.
Achieved significant improvements compared to past methods that scale exponentially.

Chen et al. (2026) studied this question.

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Randomized trial derives a polynomial-time algorithm for optimal stopping, suggesting practical improvements for high-dimensional problems.