New Lower Bounds for the Minimum Singular Value in Matrix Selection

TL;DR

This paper introduces a new method using interlacing polynomials to derive tighter lower bounds for the minimum singular value of submatrices, especially when the submatrix size is close to the original matrix dimensions.

Contribution

It applies the interlacing polynomial technique directly to roots and coefficients, improving bounds for the minimum singular value in matrix selection problems.

Findings

Tighter lower bounds for minimum singular values when submatrix size is close to original.

Improved results for the case where $AA^{ op}= ext{I}_n$ and $k=n$, surpassing Hong-Pan's bounds.

Enhanced understanding of submatrix spectral properties using polynomial interlacing.

Abstract

The objective of the matrix selection problem is to select a submatrix $A_{S}\in \mathbb{R}^{n\times k}$ from $A\in \mathbb{R}^{n\times m}$ such that its minimum singular value is maximized. In this paper, we employ the interlacing polynomial method to investigate this problem. This approach allows us to identify a submatrix $A_{S_0}\in \mathbb{R}^{n\times k}$ and establish a lower bound for its minimum singular value. Specifically, unlike common interlacing polynomial approaches that estimate the smallest root of the expected characteristic polynomial via barrier functions, we leverage the direct relationship between roots and coefficients. This leads to a tighter lower bound when $k$ is close to $n$. For the case where $AA^{\top}=\mathbb{I}_n$ and $k=n$, our result improves the well-known result by Hong-Pan, which involves extracting a basis from a tight frame and establishing a lower bound for the minimum singular value of the basis matrix.