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Derivatives with respect to metrics and applications: Subgradient Marching Algorithm

Benmansour, Fethallah; Carlier, Guillaume; Peyré, Gabriel; Santambrogio, Filippo (2010), Derivatives with respect to metrics and applications: Subgradient Marching Algorithm, Numerische Mathematik, 116, 3, p. 357-381. http://dx.doi.org/10.1007/s00211-010-0305-8

Type
Article accepté pour publication ou publié
External document link
http://hal.archives-ouvertes.fr/hal-00360971/en/
Date
2010
Journal name
Numerische Mathematik
Volume
116
Number
3
Publisher
Springer
Pages
357-381
Publication identifier
http://dx.doi.org/10.1007/s00211-010-0305-8
Metadata
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Author(s)
Benmansour, Fethallah
Carlier, Guillaume
Peyré, Gabriel
Santambrogio, Filippo
Abstract (EN)
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.
Subjects / Keywords
Travel Time Tomography; Traffic Congestion; Fast Marching Method; Subgradient Descent; Eikonal Equation; Geodesics

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