The -Levy-Grothendieck problem and norms of Levy matrices
arXiv:2404.18299
Abstract
Given an matrix and , consider the following quadratic optimization problem referred to as the -Grothendieck problem: \begin{align}M_r(A_n)\coloneqq\max_{\boldsymbol{x}\in\mathbb{R}^n:\|\boldsymbol{x}\|_r\leq1}\boldsymbol{x}^{\top} A_n \boldsymbol{x},\end{align} as well as the operator norm of the matrix , defined as \begin{align}\|A_n\|_{r \rightarrow p}\coloneqq \sup _{\boldsymbol{x}\in\mathbb{R}^n:\|\boldsymbol{x}\|_r \leq 1}\|A_n \boldsymbol{x}\|_p,\end{align} where denotes the -norm of the vector . This work analyzes high-dimensional asymptotics of these quantities when are symmetric random matrices with independent and identically distributed heavy-tailed upper-triangular entries with index . When (respectively, ) and , suitably scaled versions of and are shown to converge to a Fréchet distribution as . In contrast, when (respectively, ), it is shown that there exists such that for every , suitably scaled versions of and converge to the power of a stable distribution. Furthermore, it is shown that there exists such that when , the latter convergence result holds only when the matrix entries are centered; when the entries have non-zero mean, a different limit arises after additional centering and scaling. As a corollary, these results yield a characterization of the limiting ground state of the Levy spin glass when . The analysis uses a combination of tools from the theory of heavy-tailed distributions, the nonlinear power method and concentration inequalities.
38 pages, 3 figures