# Constrained dynamical optimal transport and its Lagrangian formulation

arXiv: https://arxiv.org/abs/1807.00937 · HTML: https://mlsanity.com/paper/1807.00937/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2018.

## Built on by (most cited first, 1 of 1)

- [Mean Field Game GAN](https://mlsanity.com/paper/2103.07855/) (2021)

## 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.