Probabilistic Logarithmic-Space Algorithms for Laplacian Solvers

Authors Dean Doron, François Le Gall, Amnon Ta-Shma

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Dean Doron
François Le Gall
Amnon Ta-Shma

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Dean Doron, François Le Gall, and Amnon Ta-Shma. Probabilistic Logarithmic-Space Algorithms for Laplacian Solvers. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 81, pp. 41:1-41:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)


A recent series of breakthroughs initiated by Spielman and Teng culminated in the construction of nearly linear time Laplacian solvers, approximating the solution of a linear system Lx=b, where L is the normalized Laplacian of an undirected graph. In this paper we study the space complexity of the problem. Surprisingly we are able to show a probabilistic, logspace algorithm solving the problem. We further extend the algorithm to other families of graphs like Eulerian graphs (and directed regular graphs) and graphs that mix in polynomial time. Our approach is to pseudo-invert the Laplacian, by first "peeling-off" the problematic kernel of the operator, and then to approximate the inverse of the remaining part by using a Taylor series. We approximate the Taylor series using a previous work and the special structure of the problem. For directed graphs we exploit in the analysis the Jordan normal form and results from matrix functions.
  • Laplacian solvers
  • Randomized logspace
  • Bounded-space complexity classes
  • Random walks
  • Matrix computation


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