arXiv subjects

Matvei Zhukov

Publications and source records attributed to Matvei Zhukov.

3 recordsLinked to original sources

arXiv2026

Matrix nearness problems: Do real inputs admit real solutions?

Given a real square matrix $A$ and a nonempty closed target set of complex matrices $\mathcal E$, invariant by complex conjugation and containing real matrices, does the subset of $\mathcal{E}$ consisting of the matrices nearest to $A$ in the Frobenius distance always contain a real matrix? We give negative answers when $\mathcal{E}$ is either the set of normal matrices (solving a question posed by N. Higham) or the set of matrices whose eigenvalues lie in the closed left half-plane (solving a question posed by the first author and F. Poloni). For the latter problem, we also argue that the ratio between the real and complex distances is unbounded in every dimension $n\geq 3$, and this holds for every distance induced by a unitarily invariant norm. For both problems and $n \geq 3$, we show that a real input $A$ uniformly drawn from the unit sphere $\|A\|_F=1$ has no real minimizer over $\mathcal{E}$ with probability strictly between $0$ and $1$. The paper is complemented by some further results that are valid for more general target sets $\mathcal{E}$.

math.NA↗

arXiv2026

Closest Normal Matrix Found Again Using Riemannian Optimization

We propose an approach based on Riemannian optimization to compute a nearest normal matrix to a given one. The problem can be formulated as the minimization of a smooth function either on the manifold $U(n)$ of unitary matrices of size n or on the flag manifold $U (n)/U (1)^n$. The flag manifold is particularly suitable for theoretical analysis; we characterize the global maximum of the objective function and prove that, for generic inputs, its local minimizers are finitely many and isolated; in turn, this implies the original nearest normal matrix problem generically has finitely many local minimizers, all with distinct eigenvalues. We also develop a Riemannian trust-region method that improves substantially on classical algorithms and can handle considerably larger matrices, as well as a variant for computing the nearest real normal matrix. The paper is complemented by extensive numerical experiments.

math.NA↗

arXiv2025

Sharp constants relating the sub-Gaussian norm and the sub-Gaussian parameter

We determine the optimal constants in the classical inequalities relating the sub-Gaussian norm $\|X\|_{ψ_2}$ and the sub-Gaussian parameter $σ_X$ for centered real-valued random variables. We show that $\sqrt{3/8} \cdot \|X\|_{ψ_2} \le σ_X \le \sqrt{\log 2} \cdot \|X\|_{ψ_2}$, and that both bounds are sharp, attained by the standard Gaussian and Rademacher distributions, respectively.

math.PR↗