172406
20181203022557.0
10.1007/s00211-010-0305-8
doi
000281397600001
ISI
ARTICLE
Derivatives with respect to metrics and applications: subgradient marching algorithm
2010
2010
Journal Articles
This paper introduces a subgradient descent algorithm to compute a Riemannian metric that minimizes an energy involving geodesic distances. The heart of the method is the Subgradient Marching Algorithm to compute the derivative of the geodesic distance with respect to the metric. The geodesic distance being a concave function of the metric, this algorithm computes an element of the subgradient in O(N (2) log(N)) operations on a discrete grid of N points. It performs a front propagation that computes a subgradient of a discrete geodesic distance. We show applications to landscape modeling and to traffic congestion. Both applications require the maximization of geodesic distances under convex constraints, and are solved by subgradient descent computed with our Subgradient Marching. We also show application to the inversion of travel time tomography, where the recovered metric is the local minimum of a non-convex variational problem involving geodesic distances.
Hamilton-Jacobi Equations
Travel-Time Tomography
Traffic Congestion
Equilibria
Benmansour, F.
Carlier, G.
Peyre, G.
Santambrogio, F.
357-381
Numerische Mathematik
116
CVLAB
252087
U10659
oai:infoscience.tind.io:172406
article
IC
112366
EPFL-ARTICLE-172406
EPFL
PUBLISHED
REVIEWED
ARTICLE