Deconvolution on graphs via linear programming
Functional Analysis
2026-05-19 v2
Abstract
The main challenge addressed in this paper is to identify individual terms in a superposition of heat kernels on a graph. We establish geometric conditions on the vertices at which these heat kernels are centered and find bounds on the time parameter governing the evolution under the heat semigroup that guarantee a successful recovery. This result can be viewed as a type of deconvolution on a graph. A first main result addresses the setting of a common time parameter for all the heat kernels. We also treat a more general setting when the time parameter depends on the location at which the heat kernel is centered.
Cite
@article{arxiv.2305.02635,
title = {Deconvolution on graphs via linear programming},
author = {Bernhard G. Bodmann and Jennifer J. May},
journal= {arXiv preprint arXiv:2305.02635},
year = {2026}
}
Comments
14 pages, AMS LaTeX; update contains corrections and section on approximate duals and noisy recovery