Alphabeta Math
LemmaStatement: AI-adaptedProof: AI-adaptedPipeline-generatedjudge pass (gpt-6-luna)
How statement and proof provenance work

The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.

  • Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
  • AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
  • AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.

These labels describe origin, not correctness: citations and verification chips remain separate evidence.

A matrix-depth maximizer exists on the bounded covariance classes

Statement

For every integer p≥1, every nonempty finite sample in Rp, and every M>0, the dense bounded-class argmax correspondence Argmax⁡Γ∈Fp(βM)DSp−1(Γ,Pn) from Matrix depth and covariance estimators is nonempty compact. If 1≤s≤p/2, the single-block sparse bounded-class argmax Argmax⁡Γ∈Fp,s(βM)DUs(Γ,Pn) is also nonempty compact.

Facts & Assumptions

Given: An integer p≥1, a nonempty finite sample in Rp, its empirical law Pn, a bound M>0, and, for the sparse case, 1≤s≤p/2.

[F1]

Matrix depth and covariance estimators defines empirical matrix depth as the infimum over directions of the minimum of the two empirical weak-tail counts.

[F4]

Upper semicontinuous real map on a topological space: a real map is upper semicontinuous exactly when its strict sublevel sets are open, equivalently its weak superlevel sets are closed.

[F5]

Semicontinuous extreme value theorem on compact Euclidean sets and A closed subset of a compact metric space is compact: an upper semicontinuous real map on a nonempty compact Euclidean set attains its maximum, and a closed subset of a compact space is compact.

Proof

1.1F2F3algebra

Identify the symmetric matrices with their coordinates in Rp2. The dense class K=Fp(βM) is bounded because each entry satisfies ∣Γij∣=∣eiTΓej∣≤∥Γ∥op≤βM by [F3]. It is closed: if Γk→Γ coordinatewise with Γk∈K, then Γ is symmetric, vTΓv=lim⁡kvTΓkv≥0 for every v, and ∥Γv∥2=lim⁡k∥Γkv∥2≤βM∥v∥2 for every v. By the operator-norm definition in [F2], this gives ∥Γ∥op≤βM. Thus K is closed and bounded, so [F3] makes it compact.

1.2F1F4algebra

Fix u and write q(Γ)=uTΓu and ri=(uTxi)2. If Γk→Γ, then q(Γk)→q(Γ). For each i, if ri<q(Γ) or ri>q(Γ), the two weak-tail indicators eventually keep their limiting membership; if ri=q(Γ), the observation is counted in both weak tails at Γ. Therefore the limsup of each finite tail count is at most its value at Γ, and the limsup of their minimum is at most the minimum of their values there. The minimum of the two empirical tails is upper semicontinuous in Γ. The infimum over u remains upper semicontinuous because each strict sublevel set of the infimum is the union of the corresponding open strict sublevel sets from [F4]. Hence Γ↦DU(Γ,Pn) is upper semicontinuous on either search class.

2.1F2F5step 1.1

For each fixed S⊆[p] with ∣S∣≤s, requiring the off-diagonal entries outside S×S to vanish is a finite collection of closed coordinate equalities. Its intersection with the dense compact class is compact by [F5]. There are finitely many such S, and their union is Fp,s(βM) by [F2]. A finite union of compact sets is compact, since any open cover has a finite subcover on each of its finitely many members.

3.1F2F4F5step 1.1step 2.1step 1.2construct∎

Both applicable search classes are nonempty because they contain the zero matrix. By steps 1.1–1.2 they are compact Euclidean sets. By step 1.2, their empirical depth functions are upper semicontinuous, so [F5] gives a maximizer on each applicable class. Its argmax is the nonempty weak superlevel set at that maximum, closed by [F4] and compact by [F5]. This proves the claim.

Depends on

Used by

Cited to discharge well-definedness by Matrix depth and covariance estimators.

Dependency tree · two levels

63 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.

Sources