2020 IEEE 61st Annual Symposium on Foundations of Computer Science (FOCS) Algorithms and Hardness for Linear Algebra on Geometric Graphs Josh Alman Timothy Chu Aaron Schild Zhao Song
[email protected] [email protected] [email protected] [email protected] Harvard U. Carnegie Mellon U. U. of Washington Columbia, Princeton, IAS d d Abstract—For a function K : R × R → R≥0, and a set learning. The first task is a celebrated application of the P {x ,...,x }⊂Rd n K G P = 1 n of points, the graph P of fast multipole method of Greengard and Rokhlin [GR87], n is the complete graph on nodes where the weight between [GR88], [GR89], voted one of the top ten algorithms of the nodes i and j is given by K(xi,xj ). In this paper, we initiate the study of when efficient spectral graph theory is possible twentieth century by the editors of Computing in Science and on these graphs. We investigate whether or not it is possible Engineering [DS00]. The second task is spectral clustering to solve the following problems in n1+o(1) time for a K-graph [NJW02], [LWDH13], a popular algorithm for clustering o(1) GP when d<n : data. The third task is to label a full set of data given only • Multiply a given vector by the adjacency matrix or a small set of partial labels [Zhu05b], [CSZ09], [ZL05], Laplacian matrix of GP which has seen increasing use in machine learning. One • G Find a spectral sparsifier of P notable method for performing semi-supervised learning is • Solve a Laplacian system in GP ’s Laplacian matrix the graph-based Laplacian regularizer method [LSZ+19b], For each of these problems, we consider all functions of the K u, v f u − v2 f R → R [ZL05], [BNS06], [Zhu05a].