arXiv subjects

Zili Xu

Publications and source records attributed to Zili Xu.

9 recordsLinked to original sources

arXiv2025

Generalized Interlacing Families: New Error Bounds for CUR Matrix Decompositions

This paper introduces the concept of generalized interlacing families of polynomials, which extends the classical interlacing polynomial method to handle polynomials of varying degrees. We establish a fundamental property for these families, proving the existence of a polynomial with a desired degree whose smallest root is greater than or equal to the smallest root of the expected polynomial. Applying this framework to the generalized CUR matrix approximation problem, we derive a theoretical upper bound on the spectral norm of a residual matrix, expressed in terms of the largest root of the expected polynomial. We then explore two important special cases: the classical CUR matrix decompositions and the row subset selection problem. For classical CUR matrix decompositions, we derive an explicit upper bound for the largest root of the expected polynomial. This yields a tighter spectral norm error bound for the residual matrix compared to many existing results. Furthermore, we present a deterministic polynomial-time algorithm for solving the classical CUR problem under certain matrix conditions. For the row subset selection problem, we establish the first known spectral norm error bound. This paper extends the applicability of interlacing families and deepens the theoretical foundations of CUR matrix decompositions and related approximation problems.

math.RA↗

arXiv2025

Optimal Frames for Phase Retrieval from Edge Vectors of Optimal Polygons

This paper aims to characterize the optimal frame for phase retrieval, defined as the frame whose condition number for phase retrieval attains its minimal value. In the context of the two-dimensional real case, we reveal the connection between optimal frames for phase retrieval and the perimeter-maximizing isodiametric problem, originally proposed by Reinhardt in 1922. Our work establishes that every optimal solution to the perimeter-maximizing isodiametric problem inherently leads to an optimal frame in ${\mathbb R}^2$. By recasting the optimal polygons problem as one concerning the discrepancy of roots of unity, we characterize all optimal polygons. Building upon this connection, we then characterize all optimal frames with $m$ vectors in ${\mathbb R}^2$ for phase retrieval when $m \geq 3$ has an odd factor. As a key corollary, we show that the harmonic frame $E_m \subset {\mathbb R}^2$ is {\em not} optimal for any even integer $m \geq 4$. This finding disproves a conjecture proposed by Xia, Xu, and Xu [{\em Math. Comp.}, 94 (2025), pp.~2931--2960]. Previous work has established that $E_m$ is indeed optimal when $m$ is an odd integer.

cs.IT↗

arXiv2025

Existence and nonexistence of spherical $5$-designs of minimal type

This paper investigates the existence and properties of spherical $5$-designs of minimal type. We focus on two cases: tight spherical $5$-designs and antipodal spherical $4$-distance $5$-designs. We prove that a tight spherical $5$-design is of minimal type if and only if it possesses a specific $Q$-polynomial coherent configuration structure. For tight spherical $5$-designs in $\mathbb{R}^d$ of minimal type, we demonstrate that half of the derived code forms an equiangular tight frames (ETF) with parameters $(d-1, \frac{(d-1)(d+1)}{3})$. This provides a sufficient condition for constructing such ETFs from maximal ETFs with parameters $(d, \frac{d(d+1)}{2})$. Moreover, we establish that tight spherical $5$-designs of minimal type cannot exist if the dimension $d$ satisfies a certain arithmetic condition, which holds for infinitely many values of $d$, including $d=119$ and $527$. For antipodal spherical $4$-distance $5$-designs, we utilize valency theory to derive necessary conditions for certain special types of antipodal spherical $4$-distance $5$-designs to be of minimal type.

math.CO↗

arXiv2024

Stability in Phase Retrieval: Characterizing Condition Numbers and the Optimal Vector Set

In this paper, we primarily focus on analyzing the stability property of phase retrieval by examining the bi-Lipschitz property of the map $Φ_{\boldsymbol{A}}(\boldsymbol{x})=|\boldsymbol{A}\boldsymbol{x}|\in \mathbb{R}_+^m$, where $\boldsymbol{x}\in \mathbb{H}^d$ and $\boldsymbol{A}\in \mathbb{H}^{m\times d}$ is the measurement matrix for $\mathbb{H}\in\{\mathbb{R},\mathbb{C}\}$. We define the condition number $β_{\boldsymbol{A}}=\frac{U_{\boldsymbol{A}}}{L_{\boldsymbol{A}}}$, where $L_{\boldsymbol{A}}$ and $U_{\boldsymbol{A}}$ represent the optimal lower and upper Lipschitz constants, respectively. We establish the first universal lower bound on $β_{\boldsymbol{A}}$ by demonstrating that for any ${\boldsymbol{A}}\in\mathbb{H}^{m\times d}$, \begin{equation*} β_{\boldsymbol{A}}\geq β_0^{\mathbb{H}}=\begin{cases} \sqrt{\fracπ{π-2}}\,\,\approx\,\, 1.659 & \text{if $\mathbb{H}=\mathbb{R}$,}\\ \sqrt{\frac{4}{4-π}}\,\,\approx\,\, 2.159 & \text{if $\mathbb{H}=\mathbb{C}$.} \end{cases} \end{equation*} We prove that the condition number of a standard Gaussian matrix in $\mathbb{H}^{m\times d}$ asymptotically matches the lower bound $β_0^{\mathbb{H}}$ for both real and complex cases. This result indicates that the constant lower bound $β_0^{\mathbb{H}}$ is asymptotically tight, holding true for both the real and complex scenarios. As an application of this result, we utilize it to investigate the performance of quadratic models for phase retrieval. Lastly, we establish that for any odd integer $m\geq 3$, the harmonic frame $\boldsymbol{A}\in \mathbb{R}^{m\times 2}$ possesses the minimum condition number among all $\boldsymbol{A}\in \mathbb{R}^{m\times 2}$. We are confident that these findings carry substantial implications for enhancing our understanding of phase retrieval.

cs.IT↗

arXiv2024

Interlacing Polynomial Method for the Column Subset Selection Problem

This paper investigates the spectral norm version of the column subset selection problem. Given a matrix $\mathbf{A}\in\mathbb{R}^{n\times d}$ and a positive integer $k\leq\text{rank}(\mathbf{A})$, the objective is to select exactly $k$ columns of $\mathbf{A}$ that minimize the spectral norm of the residual matrix after projecting $\mathbf{A}$ onto the space spanned by the selected columns. We use the method of interlacing polynomials introduced by Marcus-Spielman-Srivastava to derive a new upper bound on the minimal approximation error. This new bound is asymptotically sharp when the matrix $\mathbf{A}\in\mathbb{R}^{n\times d}$ obeys a spectral power-law decay. The relevant expected characteristic polynomials can be written as an extension of the expected polynomial for the restricted invertibility problem, incorporating two extra variable substitution operators. Finally, we propose a deterministic polynomial-time algorithm that achieves this error bound up to a computational error.

cs.DS↗

arXiv2023

Interlacing Polynomial Method for Matrix Approximation via Generalized Column and Row Selection

This paper delves into the spectral norm aspect of the Generalized Column and Row Subset Selection (GCRSS) problem. Given a target matrix $\mathbf{A}\in \mathbb{R}^{n\times d}$, the objective of GCRSS is to select a column submatrix $\mathbf{B}_{:,S}\in\mathbb{R}^{n\times k}$ from the source matrix $\mathbf{B}\in\mathbb{R}^{n\times d_B}$ and a row submatrix $\mathbf{C}_{R,:}\in\mathbb{R}^{r\times d}$ from the source matrix $\mathbf{C}\in\mathbb{R}^{n_C\times d}$, such that the residual matrix $(\mathbf{I}_n-\mathbf{B}_{:,S}\mathbf{B}_{:,S}^{\dagger})\mathbf{A}(\mathbf{I}_d-\mathbf{C}_{R,:}^{\dagger} \mathbf{C}_{R,:})$ has a small spectral norm. By employing the method of interlacing polynomials, we show that the smallest possible spectral norm of a residual matrix can be bounded by the largest root of a related expected characteristic polynomial. A deterministic polynomial time algorithm is provided for the spectral norm case of the GCRSS problem. We next focus on two specific GCRSS scenarios: the Generalized Column Subset Selection (GCSS) problem ($r=0$), and the submatrix selection problem ($\mathbf{B}=\mathbf{C}=\mathbf{I}_d$). In the GCSS scenario, we connect the expected characteristic polynomials to the convolution of multi-affine polynomials, leading to the derivation of the first provable reconstruction bound on the spectral norm of a residual matrix. In the submatrix selection scenario, we show that for any sufficiently small $\varepsilon>0$ and any square matrix $\mathbf{A}\in\mathbb{R}^{d\times d}$, there exist two subsets $S\subset [d]$ and $R\subset [d]$ of sizes $O(d\cdot \varepsilon^2)$ such that $\Vert\mathbf{A}_{S,R}\Vert_2\leq \varepsilon\cdot \Vert\mathbf{A}\Vert_2$.

math.FA↗

arXiv2021

Improved bounds in Weaver's ${\rm KS}_r$ conjecture for high rank positive semidefinite matrices

Recently Marcus, Spielman and Srivastava proved Weaver's ${\rm{KS}}_r$ conjecture, which gives a positive solution to the Kadison-Singer problem. Cohen and Brändén independently extended this result to obtain the arbitrary-rank version of Weaver's ${\rm{KS}}_r$ conjecture. In this paper, we present a new bound in Weaver's ${\rm{KS}}_r$ conjecture for the arbitrary-rank case. To do that, we introduce the definition of $(k,m)$-characteristic polynomials and employ it to improve the previous estimate on the largest root of the mixed characteristic polynomials. For the rank-one case, our bound agrees with the Bownik-Casazza-Marcus-Speegle's bound when $r=2$ and with the Ravichandran-Leake's bound when $r>2$. For the higher-rank case, we sharpen the previous bounds from Cohen and from Brändén .

math.CO↗

arXiv2020

Bounds on antipodal spherical designs with few angles

A finite subset $X$ on the unit sphere $\mathbb{S}^{d-1}$ is called an $s$-distance set with strength $t$ if its angle set $A(X):=\{\langle \mathbf{x},\mathbf{y}\rangle : \mathbf{x},\mathbf{y}\in X,\mathbf{x}\neq\mathbf{y} \}$ has size $s$, and $X$ is a spherical $t$-design but not a spherical $(t+1)$-design. In this paper, we consider to estimate the maximum size of such antipodal set for small $s$. First, we improve the known bound on $|X|$ for each even integer $s\in[\frac{t+5}{2}, t+1]$ when $t\geq 3$. We next focus on two special cases: $s=3,\ t=3$ and $s=4,\ t=5$. Estimating the size of $X$ for these two cases is equivalent to estimating the size of real equiangular tight frames (ETFs) and Levenstein-equality packings, respectively. We first improve the previous estimate on the size of real ETFs and Levenstein-equality packings. This in turn gives a bound on $|X|$ when $s=3,\ t=3$ and $s=4,\ t=5$, respectively.

math.CO↗

arXiv2020

The minimizers of the $p$-frame potential

For any positive real number $p$, the $p$-frame potential of $N$ unit vectors $X:=\{\mathbf x_1,\ldots,\mathbf x_N\}\subset \mathbb R^d$ is defined as ${\rm FP}_{p,N,d}(X)=\sum_{i\neq j}|\langle \mathbf x_i,\mathbf x_j\rangle |^p$. In this paper, we focus on the special case $N=d+1$ and establish the unique minimizer of ${\rm FP}_{p,d+1,d}$ for $p\in (0,2)$. Our results completely solve the minimization problem of $p$-frame potential when $N=d+1$, which confirms a conjecture posed by Chen, Gonzales, Goodman, Kang and Okoudjou.

math.FA↗