Entropy and Complexity of a Path in Sub-riemannian Geometry
نویسنده
چکیده
We characterize the geometry of a path in a sub-Riemannian manifold using two metric invariants, the entropy and the complexity. The entropy of a subset A of a metric space is the minimum number of balls of a given radius ε needed to cover A. It allows one to compute the Hausdorff dimension in some cases and to bound it from above in general. We define the complexity of a path in a subRiemannian manifold as the infimum of the lengths of all trajectories contained in an ε-neighborhood of the path, having the same extremities as the path. The concept of complexity for paths was first developed to model the algorithmic complexity of the nonholonomic motion planning problem in robotics. In this paper, our aim is to estimate the entropy, Hausdorff dimension and complexity for a path in a general sub-Riemannian manifold. We construct first a norm ‖ · ‖ε on the tangent space that depends on a parameter ε > 0. Our main result states then that the entropy of a path is equivalent to the integral of this ε-norm along the path. As a corollary we obtain upper and lower bounds for the Hausdorff dimension of a path. Our second main result is that complexity and entropy are equivalent for generic paths. We give also a computable sufficient condition on the path for this equivalence to happen. Mathematics Subject Classification. 53C17. Received July 18, 2002. Revised February 27, 2003.
منابع مشابه
On Hausdorff Measures of Curves in Sub-Riemannian Geometry
In sub-Riemannian geometry, the length of a non-horizontal path is not defined (or is equal to +∞). However several other notions allow to measure a path, such as the Hausdorff measures, the class of k-dimensional lengths introduced in [2], or notions based on approximations by discrete sets, like the nonholonomic interpolation complexity and the entropy (see [8, 10]). The purpose of this paper...
متن کاملA Geometry Preserving Kernel over Riemannian Manifolds
Abstract- Kernel trick and projection to tangent spaces are two choices for linearizing the data points lying on Riemannian manifolds. These approaches are used to provide the prerequisites for applying standard machine learning methods on Riemannian manifolds. Classical kernels implicitly project data to high dimensional feature space without considering the intrinsic geometry of data points. ...
متن کاملMorse-Sard type results in sub-Riemannian geometry
Let (M, ∆, g) be a sub-Riemannian manifold and x0 ∈ M . Assuming that Chow’s condition holds and that M endowed with the subRiemannian distance is complete, we prove that there exists a dense subset N1 of M such that for every point x of N1, there is a unique minimizing path steering x0 to x, this trajectory admitting a normal extremal lift. If the distribution ∆ is everywhere of corank one, we...
متن کاملAnisotropically Weighted and Nonholonomically Constrained Evolutions on Manifolds
We present evolution equations for a family of paths that results from anisotropically weighting curve energies in non-linear statistics of manifold valued data. This situation arises when performing inference on data that have non-trivial covariance and are anisotropic distributed. The family can be interpreted as most probable paths for a driving semi-martingale that through stochastic develo...
متن کاملIdentification of Riemannian foliations on the tangent bundle via SODE structure
The geometry of a system of second order differential equations is the geometry of a semispray, which is a globally defined vector field on TM. The metrizability of a given semispray is of special importance. In this paper, the metric associated with the semispray S is applied in order to study some types of foliations on the tangent bundle which are compatible with SODE structure. Indeed, suff...
متن کامل