Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/65861
Title: Computing eigenvalues of large scale sparse tensors arising from a hypergraph
Authors: Chang, J
Chen, Y
Qi, L
Keywords: Eigenvalue
Hypergraph
L-BFGS
Laplacian tensor
Large scale tensor
Lojasiewicz inequality
Sparse tensor
Spherical optimization
Issue Date: 2016
Publisher: Society for Industrial and Applied Mathematics
Source: SIAM journal on scientific computing, 2016, v. 38, no. 6, p. A3618-A3643 How to cite?
Journal: SIAM journal on scientific computing 
Abstract: The spectral theory of higher-order symmetric tensors is an important tool for revealing some important properties of a hypergraph via its adjacency tensor, Laplacian tensor, and signless Laplacian tensor. Owing to the sparsity of these tensors, we propose an efficient approach to calculate products of these tensors and any vectors. By using the state-of-the-art L-BFGS approach, we develop a first-order optimization algorithm for computing H- and Z-eigenvalues of these large scale sparse tensors (CEST). With the aid of the Lojasiewicz inequality, we prove that the sequence of iterates generated by CEST converges to an eigenvector of the tensor. When CEST is started from multiple random initial points, the resulting best eigenvalue could touch the extreme eigenvalue with a high probability. Finally, numerical experiments on small hypergraphs show that CEST is efficient and promising. Moreover, CEST is capable of computing eigenvalues of tensors related to a hypergraph with millions of vertices.
URI: http://hdl.handle.net/10397/65861
ISSN: 1064-8275
EISSN: 1095-7197
DOI: 10.1137/16M1060224
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