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Guo, Gaoyue; Obłój, Jan

Computational methods for martingale optimal transport problems. (English) Zbl 1433.49043

Ann. Appl. Probab. 29, No. 6, 3311-3347 (2019).
Summary: We develop computational methods for solving the martingale optimal transport (MOT) problem – a version of the classical optimal transport with an additional martingale constraint on the transport’s dynamics. We prove that a general, multi-step multi-dimensional, MOT problem can be approximated through a sequence of linear programming (LP) problems which result from a discretization of the marginal distributions combined with an appropriate relaxation of the martingale condition. Further, we establish two generic approaches for discretising probability distributions, suitable respectively for the cases when we can compute integrals against these distributions or when we can sample from them. These render our main result applicable and lead to an implementable numerical scheme for solving MOT problems. Finally, specialising to the one-step model on real line, we provide an estimate of the convergence rate which, to the best of our knowledge, is the first of its kind in the literature.

Cited in 38 Documents

MSC:

49M25 Discrete approximations in optimal control
60H99 Stochastic analysis
90C08 Special problems of linear programming (transportation, multi-index, data envelopment analysis, etc.)
60G46 Martingales and classical analysis
49Q99 Manifolds and measure-geometric topics

Keywords:

martingale optimal transport; martingale relaxation; robust hedging; duality; discretization of measure; linear programming
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Full Text: DOI arXiv Euclid

References:

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