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Degeneracy of the Operator-Valued Poisson Kernel near the Numerical Range Boundary

Publié
Serveur de preprints
Preprints.org
DOI
10.20944/preprints202602.0563.v2

Let $A\in\C^{d\times d}$ and let W(A)W(A) denote its numerical range. For a bounded convex domain $\Omega\subset\C$ with C1C^1 boundary containing $\spec(A)$, consider the operator-valued boundary kernel \[ P_\Omega(\sigma,A)\;:=\;\Real\!\Bigl(n_\Omega(\sigma)\,(\sigma\Id-A)^{-1}\Bigr), \qquad \sigma\in\partial\Omega, \] where nΩ(σ)n_\Omega(\sigma) is the outward unit normal at σ\sigma. For convex Ω\Omega with W(A)⊂ΩW(A)\subset\Omega, this kernel is positive definite on ∂Ω\partial\Omega and underlies boundary-integral functional calculi and spectral-set bounds in the sense of Delyon--Delyon and Crouzeix.We analyze the opposite limiting regime Ω↓W(A)\Omega\downarrow W(A). Along any C1C^1 convex exhaustion Ωε↓W(A)\Omega_\varepsilon\downarrow W(A), if σε∈∂Ωε\sigma_\varepsilon\in\partial\Omega_\varepsilon approaches a non-spectral boundary point $\sigma_0\in\partial W(A)\setminus\spec(A)$ with convergent outward normals nΩε(σε)→nn_{\Omega_\varepsilon}(\sigma_\varepsilon)\to n, then λmin⁡(PΩε(σε,A))→0\lambda_{\min}(P_{\Omega_\varepsilon}(\sigma_\varepsilon,A))\to 0 and the associated min-eigenvector directions converge (up to subsequences and phases) to the canonical subspace $(\sigma_0\Id-A)\mathcal M(n)$ determined by the maximal eigenspace of $H(n)=\Real(\overline{n}A)$.Quantitatively, we obtain two-sided bounds in terms of the support-gap scalar $\delta(\sigma,n)=\Real(\overline{n}\,\sigma)-\lambda_{\max}(H(n))$, yielding a linear degeneracy rate under bounded-resolvent hypotheses and an explicit rate for outer offsets W(A)+εDW(A)+\varepsilon\mathbb{D}. Under a spectral-isolation hypothesis for λmax⁡(H(n))\lambda_{\max}(H(n)), we characterize the entire collapsing eigenvalue cluster under non-tangential offsets: exactly m=dim⁡M(n)m=\dim\mathcal M(n) eigenvalues decay as O(ε)O(\varepsilon) with a computable slope spectrum given by the eigenvalues of an explicit Gram matrix G(n,σ0)−1G(n,\sigma_0)^{-1}, while the remaining eigenvalues stay uniformly bounded away from $0.Thisyieldsarigorousfacedetectorbasedoncountingsmalleigenvalues,andtherescaledclusterisintrinsicunderarbitrary. This yields a rigorous face detector based on counting small eigenvalues, and the rescaled cluster is intrinsic under arbitrary C^1convexexhaustionsafternormalizationby convex exhaustions after normalization by \delta.Atspectralsupportpoints.At spectral support points \sigma_0\in\spec(A)\cap\partial W(A)weobtainathree−scalepicturefornonnormalmatrices:anexact we obtain a three-scale picture for nonnormal matrices: an exact 1/\varepsilonblow−upon blow-up on \Ker(\sigma_0\Id-A),an, an O(\varepsilon)collapsingclusteron collapsing cluster on \mathcal M(n)\ominus\Ker(\sigma_0\Id-A)withanexplicitslopespectrum,andan with an explicit slope spectrum, and an O(1)bulkseparatedfrom bulk separated from 0.Fornormalmatriceswecomputethespectrumof. For normal matrices we compute the spectrum of P_\Omega(\sigma,A)$ explicitly, recovering a simple dichotomy at spectral support points in terms of whether the supporting face contains multiple eigenvalues. Finally, we include reproducible numerical experiments (Python) validating the predicted slopes and splittings.

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