138 research outputs found
Numerical Optimization for Symmetric Tensor Decomposition
We consider the problem of decomposing a real-valued symmetric tensor as the
sum of outer products of real-valued vectors. Algebraic methods exist for
computing complex-valued decompositions of symmetric tensors, but here we focus
on real-valued decompositions, both unconstrained and nonnegative, for problems
with low-rank structure. We discuss when solutions exist and how to formulate
the mathematical program. Numerical results show the properties of the proposed
formulations (including one that ignores symmetry) on a set of test problems
and illustrate that these straightforward formulations can be effective even
though the problem is nonconvex
Shifted Power Method for Computing Tensor Eigenpairs
Recent work on eigenvalues and eigenvectors for tensors of order m >= 3 has
been motivated by applications in blind source separation, magnetic resonance
imaging, molecular conformation, and more. In this paper, we consider methods
for computing real symmetric-tensor eigenpairs of the form Ax^{m-1} = \lambda x
subject to ||x||=1, which is closely related to optimal rank-1 approximation of
a symmetric tensor. Our contribution is a shifted symmetric higher-order power
method (SS-HOPM), which we show is guaranteed to converge to a tensor
eigenpair. SS-HOPM can be viewed as a generalization of the power iteration
method for matrices or of the symmetric higher-order power method.
Additionally, using fixed point analysis, we can characterize exactly which
eigenpairs can and cannot be found by the method. Numerical examples are
presented, including examples from an extension of the method to finding
complex eigenpairs
Triadic Measures on Graphs: The Power of Wedge Sampling
Graphs are used to model interactions in a variety of contexts, and there is
a growing need to quickly assess the structure of a graph. Some of the most
useful graph metrics, especially those measuring social cohesion, are based on
triangles. Despite the importance of these triadic measures, associated
algorithms can be extremely expensive. We propose a new method based on wedge
sampling. This versatile technique allows for the fast and accurate
approximation of all current variants of clustering coefficients and enables
rapid uniform sampling of the triangles of a graph. Our methods come with
provable and practical time-approximation tradeoffs for all computations. We
provide extensive results that show our methods are orders of magnitude faster
than the state-of-the-art, while providing nearly the accuracy of full
enumeration. Our results will enable more wide-scale adoption of triadic
measures for analysis of extremely large graphs, as demonstrated on several
real-world examples
Wedge Sampling for Computing Clustering Coefficients and Triangle Counts on Large Graphs
Graphs are used to model interactions in a variety of contexts, and there is
a growing need to quickly assess the structure of such graphs. Some of the most
useful graph metrics are based on triangles, such as those measuring social
cohesion. Algorithms to compute them can be extremely expensive, even for
moderately-sized graphs with only millions of edges. Previous work has
considered node and edge sampling; in contrast, we consider wedge sampling,
which provides faster and more accurate approximations than competing
techniques. Additionally, wedge sampling enables estimation local clustering
coefficients, degree-wise clustering coefficients, uniform triangle sampling,
and directed triangle counts. Our methods come with provable and practical
probabilistic error estimates for all computations. We provide extensive
results that show our methods are both more accurate and faster than
state-of-the-art alternatives.Comment: Full version of SDM 2013 paper "Triadic Measures on Graphs: The Power
of Wedge Sampling" (arxiv:1202.5230
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