The paper establishes an Aronson-Bénilan/Li-Yau estimate inside the JKO discretization of diffusion equations in low dimensions, giving time-step-uniform L^∞ density bounds via a maximum principle on the Hessian determinant of Brenier potentials.
Springer, 2015
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3representative citing papers
A proof blueprint establishes robust O(1/k) rates for entropic Bregman projections that scale linearly in the inverse regularization strength, instantiated as a new flow-Sinkhorn method for graph W1 with O(p diameter^3 / ε^4) complexity.
A reduced-order model for parametrized optimal transport problems using low-dimensional cone or subspace constraints and EIM-based error estimation.
citing papers explorer
-
An Aronson-B\'enilan / Li-Yau estimate in the JKO scheme in small dimension
The paper establishes an Aronson-Bénilan/Li-Yau estimate inside the JKO discretization of diffusion equations in low dimensions, giving time-step-uniform L^∞ density bounds via a maximum principle on the Hessian determinant of Brenier potentials.
-
Robust Sublinear Convergence Rates for Iterative Bregman Projections
A proof blueprint establishes robust O(1/k) rates for entropic Bregman projections that scale linearly in the inverse regularization strength, instantiated as a new flow-Sinkhorn method for graph W1 with O(p diameter^3 / ε^4) complexity.
-
A reduced-order model for parametrized Optimal Transport problems
A reduced-order model for parametrized optimal transport problems using low-dimensional cone or subspace constraints and EIM-based error estimation.