Constrained dynamical optimal transport and its Lagrangian formulation
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Abstract (arXiv)
We propose dynamical optimal transport (OT) problems constrained in a parameterized probability subset. In application problems such as deep learning, the probability distribution is often generated by a parameterized mapping function. In this case, we derive a simple formulation for the constrained dynamical OT.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). First posted on arXiv 3 Jul 2018.
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- 2021Mean Field Game GANcited by 0
- 2021Mean Field Game GAN14 Mar 2021