Scalable Bi-causal Optimal Transport via KL Relaxation and Policy Gradients
Bi-causal optimal transport (OT) is a natural framework for comparing and coupling stochastic processes under nonanticipative information constraints, with important applications in robust finance, sequential uncertainty quantification, and multistage stochastic optimization. In particular, a learned bi-causal coupling naturally serves as a simulator for generating joint sample paths that respect both prescribed marginal laws and the underlying information flow. Its practical use, however, is limited by the computational difficulty of enforcing bi-causal coupling constraints over path space, especially for continuous distributions and long horizons. We develop a scalable stochastic-optimi...
| Authors | Haoyang Cao, Jesse Hoekstra, Renyuan Xu et al. |
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| Primary category | math.OC |
| Published | 17 May 2026 |
| Last updated at source | 17 May 2026 |
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