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Positive Eigenfunctions Above the First Eigenvalue of the Affine 2-Laplacian

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Abstract

For every integer n≥2, we construct a bounded connected nonconvex domain with smooth boundary on which the affine 2-Laplacian has two nonproportional strictly positive first eigenfunctions and a strictly positive eigenfunction above the first eigenvalue. These examples give negative answers at exponent two to both the simplicity and positive-eigenvalue uniqueness questions of Haddad, Jiménez and Montenegro. The construction starts with a planar pentagon pinched into two triangular lobes. A closing odd spectral gap creates negative shear curvature at an exactly isotropic position. A global minimum comparison on the symmetry-preserving linear slice then forces every positive first affine eigenfunction to break a domain symmetry. Parameter-controlled smooth exhaustion and a balanced product lift preserve both properties in every dimension. We also obtain sharp inverse-gap curvature limits and nonparallel-hull isotropic saddles. In contrast, a normalization correspondence and Faifman and Polterovich's convex-position theorem show that on bounded convex domains the positive affine 2-eigenfunction is unique up to scale and belongs to the first affine eigenvalue.

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Available editions

  1. Edition 4 · 2026-11-02 · public
  2. Edition 3 · 2026-10-27 · public
  3. Edition 2 · 2026-10-24 · public
  4. Edition 1 · 2026-10-21 · public

Research details

Subjects
Mathematics · Analysis and Spectral Theory · Differential Equations
Keywords
affine Laplacian, positive eigenfunction, Dirichlet eigenvalue, spectral shape optimization, nonconvex polygon, second variation
Manuscript date
2026-10-20
Reader reuse
none

Scope

Smooth bounded connected nonconvex examples in every dimension n ≥ 2 with nonproportional positive first affine 2-eigenfunctions and a positive eigenfunction above the first eigenvalue; curvature and saddle refinements contrasted with positive-eigenfunction uniqueness on bounded convex domains.

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AI tools assisted research, proof development, writing, and verification. This is an author-published preprint; internal AI scrutiny is not external human refereeing.

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References and related resources

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