Efficient $\widetilde{O}(n/\epsilon)$ Spectral Sketches for the Laplacian and its Pseudoinverse
Arun Jambulapati, Aaron Sidford

TL;DR
This paper introduces nearly-linear time algorithms to create compact spectral sketches of Laplacian matrices and their pseudoinverses, significantly reducing storage and computation costs for graph analysis tasks.
Contribution
It presents the first nearly-linear time algorithms for constructing $ ilde{O}(n/ ext{epsilon})$-size spectral sketches of Laplacians and their pseudoinverses, improving previous bounds.
Findings
Achieves $ ilde{O}(n/ ext{epsilon})$-size sketches for Laplacian and pseudoinverse
Reduces sketch construction time to nearly-linear in input size
Enables efficient computation of all-pairs effective resistances
Abstract
In this paper we consider the problem of efficiently computing -sketches for the Laplacian and its pseudoinverse. Given a Laplacian and an error tolerance , we seek to construct a function such that for any vector (chosen obliviously from ), with high probability where is either the Laplacian or its pseudoinverse. Our goal is to construct such a sketch efficiently and to store it in the least space possible. We provide nearly-linear time algorithms that, when given a Laplacian matrix and an error tolerance , produce -size sketches of both and its pseudoinverse. Our algorithms improve upon the previous best sketch size of for sketching the Laplacian form by…
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Taxonomy
TopicsComputational Geometry and Mesh Generation · Advanced Memory and Neural Computing · Stochastic Gradient Optimization Techniques
