239,537 research outputs found
Notions of optimal transport theory and how to implement them on a computer
This article gives an introduction to optimal transport, a mathematical
theory that makes it possible to measure distances between functions (or
distances between more general objects), to interpolate between objects or to
enforce mass/volume conservation in certain computational physics simulations.
Optimal transport is a rich scientific domain, with active research
communities, both on its theoretical aspects and on more applicative
considerations, such as geometry processing and machine learning. This article
aims at explaining the main principles behind the theory of optimal transport,
introduce the different involved notions, and more importantly, how they
relate, to let the reader grasp an intuition of the elegant theory that
structures them. Then we will consider a specific setting, called
semi-discrete, where a continuous function is transported to a discrete sum of
Dirac masses. Studying this specific setting naturally leads to an efficient
computational algorithm, that uses classical notions of computational geometry,
such as a generalization of Voronoi diagrams called Laguerre diagrams.Comment: 32 pages, 17 figure
The inverse moment problem for convex polytopes: implementation aspects
We give a detailed technical report on the implementation of the algorithm
presented in Gravin et al. (Discrete & Computational Geometry'12) for
reconstructing an -vertex convex polytope in from the
knowledge of of its moments
Path Puzzles: Discrete Tomography with a Path Constraint is Hard
We prove that path puzzles with complete row and column information--or
equivalently, 2D orthogonal discrete tomography with Hamiltonicity
constraint--are strongly NP-complete, ASP-complete, and #P-complete. Along the
way, we newly establish ASP-completeness and #P-completeness for 3-Dimensional
Matching and Numerical 3-Dimensional Matching.Comment: 16 pages, 8 figures. Revised proof of Theorem 2.4. 2-page abstract
appeared in Abstracts from the 20th Japan Conference on Discrete and
Computational Geometry, Graphs, and Games (JCDCGGG 2017
A measure of non-convexity in the plane and the Minkowski sum
In this paper a measure of non-convexity for a simple polygonal region in the
plane is introduced. It is proved that for "not far from convex" regions this
measure does not decrease under the Minkowski sum operation, and guarantees
that the Minkowski sum has no "holes".Comment: 5 figures; Discrete and Computational Geometry, 201
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