[Lowerbounds, Upperbounds]

Algorithms are everywhere.

Speaker: Mohammad Taghi Hajiaghayi

Time: Wednesday 12-1pm

Place: NSH 1507

Title: Plane embeddings of planar graph metrics

Abstract:

Embedding metrics into constant-dimensional geometric spaces, such as the Euclidean plane, is relatively poorly understood. Motivated by applications in visualization, ad-hoc networks, and molecular reconstruction, we consider the natural problem of embedding shortest-path metrics of unweighted planar graphs (planar graph metrics) into the Euclidean plane. It is known that, in the special case of shortestpath metrics of trees, embedding into the plane requires $\Omega(pn)$ distortion in the worst case, and surprisingly, this worst-case upper bound provides the best known approximation algorithm for minimizing distortion. We answer an open question posed in this work by proving that some planar graph metrics require $\Omega(n^{2/3})$ distortion in any embedding into the plane, proving the first separation between these two types of graph metrics. We also prove that some planar graph metrics require $\Omega(n)$ distortion in any crossing-free straight-line embedding into the plane, suggesting a separation between low-distortion plane embedding and the well-studied notion of crossing-free straight-line planar drawings. Finally, on the upper-bound side, we prove that all outerplanar graph metrics can be embedded into the plane with $O(pn)$ distortion, generalizing the previous results on trees (both the worst-case bound and the approximation algorithm) and building techniques for handling cycles in plane embeddings of graph metrics.

Approximating Surfaces with Meshes

Ken Clarkson
Lucent Bell Labs

Friday March 10, 3:30PM
Wean 7220

How hard is it to approximate a smooth surface M with a piecewise-linear mesh? When M is the boundary of a convex body, remarkably tight bounds are known for the smallest Hausdorff distance possible when using a mesh with n simplices. In the case of more general surfaces, much less is understood. I’ll show that the smallest distance, when M is a d-manifold, is O(S/n)^{2/d}, where S is the integral over M of the square root of the Gaussian curvature. (The constant factor here depends only on the dimension.) Also, under some reasonably general conditions on the surface and the mesh, this expression is also a lower bound, up to a constant factor. The upper bound construction distributes the vertices of the mesh in an “epsilon-net”, in a metric based on directional curvature. The lower bound relates the volume of a simplex to its interpolation error.