New algorithms approximate multimarginal optimal transport with near-linear classical time and sublinear quantum time in the tensor dimension, plus matching query lower bounds.
Computation of Robust Option Prices via Structured Multi-Marginal Martingale Optimal Transport
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abstract
We introduce an efficient computational framework for solving a class of multi-marginal martingale optimal transport problems, which includes many robust pricing problems of large financial interest. Such problems are typically computationally challenging due to the martingale constraint, however, by extending the state space we can identify them with problems that exhibit a certain sequential martingale structure. Our method exploits such structures in combination with entropic regularisation, enabling fast computation of optimal solutions and allowing us to solve problems with a large number of marginals. We demonstrate the method by using it for computing robust price bounds for different options, such as lookback options and Asian options.
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quant-ph 1years
2026 1verdicts
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Faster Algorithms for Multimarginal Optimal Transport
New algorithms approximate multimarginal optimal transport with near-linear classical time and sublinear quantum time in the tensor dimension, plus matching query lower bounds.