We develop new techniques for proving lower bounds on the least singular value of random matrices with limited randomness. The matrices we consider have entries that are given by polynomials of a few underlying base random variables. This setting captures a core technical challenge for obtaining smoothed analysis guarantees in many algorithmic settings. Least singular value bounds often involve showing strong anti-concentration inequalities that are intricate and much less understood compared to concentration (or large deviation) bounds.
First, we introduce a general technique for proving anti-concentration that uses well-conditionedness properties of the Jacobian of a polynomial map, and show how to combine this with a hierarchical net argument to prove least singular value bounds. Our second tool is a new statement about least singular values to reason about higher-order lifts of smoothed matrices and the action of linear operators on them.
Apart from getting simpler proofs of existing smoothed analysis results, we use these tools to now handle more general families
of random matrices. This allows us to produce smoothed analysis guarantees in several previously open settings. These new settings include smoothed analysis guarantees for power sum decompositions and certifying robust entanglement of subspaces, where prior work could only establish least singular value bounds for fully random instances or only show non-robust genericity guarantees.
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Perturbation bounds for (nearly) orthogonally decomposable tensors with statistical applications
We develop deterministic perturbation bounds for singular values and vectors of orthogonally decomposable tensors, in a spirit similar to classical results for matrices such as those due to Weyl, Davis, Kahan and Wedin. Our bounds demonstrate intriguing differences between matrices and higher order tensors. Most notably, they indicate that for higher order tensors perturbation affects each essential singular value/vector in isolation, and its effect on an essential singular vector does not depend on the multiplicity of its corresponding singular value or its distance from other singular values. Our results can be readily applied and provide a unified treatment to many different problems involving higher order orthogonally decomposable tensors. In particular, we illustrate the implications of our bounds through connected yet seemingly different high-dimensional data analysis tasks: the unsupervised learning scenario of tensor SVD and the supervised task of tensor regression, leading to new insights in both of these settings.
more » « less- Award ID(s):
- 2052955
- PAR ID:
- 10389089
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Information and Inference: A Journal of the IMA
- Volume:
- 12
- Issue:
- 2
- ISSN:
- 2049-8772
- Format(s):
- Medium: X Size: p. 1044-1072
- Size(s):
- p. 1044-1072
- Sponsoring Org:
- National Science Foundation
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