Alphabeta Math
ExampleConstruction: AI-generatedVerification: AI-generatedPipeline-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.

At aspect one the reciprocal-spectrum limit diverges

Example

Assume the Axiom of Choice. Let (ξij)i,j≥1 be one infinite iid complex array with mean zero, unit second absolute moment and finite fourth moment. Let Nn be a positive integer with n/Nn→1, set Tn=In, and choose deterministic unit vectors xn∈Cn. The directional measures converge almost surely to the aspect-one Marchenko–Pastur law with density ρ1(t)=4−t/(2πt) on (0,4) and no atom at zero. For every fixed s>0, their negative-axis integrals converge to ∫(t+s)−1dμ1(t), and these limiting integrals tend to +∞ as s↓0; the smallest positive sample-covariance eigenvalue tends to zero, so there is no positive lower-edge gap for reciprocal-spectrum closure.

Facts & Assumptions

Given: AC, the nested complex iid entry law, Tn=In, aspect ratio one, and deterministic unit directions.

[A1]

The Axiom of Choice (The Axiom of Choice).

[F1]

The isotropic directional law converges to the Marchenko–Pastur law (Sample-covariance resolvent deterministic equivalents).

[F2]

Fixed negative-axis integrals converge by bounded-continuous-test convergence (Fixed negative-axis directional resolvent integrals converge).

[F3]

At aspect one the Marchenko–Pastur density is 4−t/(2πt) on (0,4) and has no zero atom (Marchenko–Pastur measure with aspect ratio gamma, The Marchenko–Pastur formula defines a probability law with the fixed-point transform).

[F4]

The finite complex directional measure is μnxn=∑λ∈spec⁡(An)∥Pλxn∥22δλ, with nonnegative weights and support on the nonnegative eigenvalues of An (Directional spectral measure of a Hermitian matrix). Weak convergence means convergence of integrals against every bounded continuous real-valued function (Weak convergence of borel probability measures).

[F5]

The isotropic limit F1,δ1 equals the aspect-one Marchenko–Pastur law because both transforms satisfy the unique fixed-point equation (Uniqueness of the deformed Marchenko–Pastur fixed point, The Marchenko–Pastur formula defines a probability law with the fixed-point transform, Stieltjes inversion and uniqueness for probability measures on the real line).

[F6]

Monotone convergence passes increasing nonnegative functions through their measure integrals (Monotone convergence for the integral).

[F7]

The Dirac measure δ1 is the unit point mass at 1 (The Dirac set function at a point).

[F8]

The nonnegative integral is monotone and homogeneous under positive finite scalars; the integral of a measurable indicator is its set measure, by the simple-integral formula and agreement with the nonnegative integral (Monotonicity and nonnegative homogeneity of the nonnegative integral, The integral of a nonnegative simple function, The nonnegative integral agrees with the simple integral on simple functions).

Verification

Given: The hypotheses and notation in the Example.

1.1givenA1F1F3F5F7algebra

With H=δ1 and c=1, the population-resolvent condition holds for every unit vector, so [F1] gives convergence to F1,δ1. The transform of the law in [F3] satisfies the same fixed-point equation; [F5] identifies F1,δ1=μ1. Substituting a1=0, b1=4 into the density in [F3] gives ρ1(t)=4−t/(2πt) on (0,4) and zero atom.

2.1F2F3F6step 1.1algebra

For fixed s>0, t↦(t+s)−1 is bounded and continuous, so [F2] gives the asserted limit of the negative-axis integrals. For 0<t<1, [F3] gives ρ1(t)≥3(2π)−1t−1/2; hence t−1ρ1(t)≥3(2π)−1t−3/2, whose integral at zero diverges. Along sk=1/k, the nonnegative functions (t+sk)−1 increase to t−1 for t>0. The law has no atom at zero by [F3], and [F6] gives ∫(t+sk)−1dμ1(t)↑∫t−1dμ1(t)=∞. Since these integrals are nondecreasing as s↓0, the full limit is +∞.

2.2F3F4F8step 1.1algebra

Work on the probability-one weak-convergence event of step 1.1. For each integer j≥1, set bj=2−j and aj=bj/2, and define the bounded continuous real function φj(t)=max⁡{0,1−∣2t−aj−bj∣/(bj−aj)} on R. It vanishes outside [aj,bj], is positive exactly on (aj,bj), and is at least 1/2 on the middle half Jj of that interval. On (aj,bj), [F3] gives ρ1(t)≥4−bj/(2πbj)>0. Applying the order and scalar rules in [F8] first to φj≥121Jj under μ1, then to this density lower bound on Jj under Lebesgue measure, gives ∫φj dμ1≥12μ1(Jj)≥(bj−aj)4−bj/(8πbj)>0, since Jj has length (bj−aj)/2. By [F4] and step 1.1, ∫φj dμnxn→∫φj dμ1, so for each j this sample integral is eventually positive. Since 0≤φj≤1(aj,bj), [F8] gives 0<∫φj dμnxn≤μnxn((aj,bj)). The finite atomic set formula in [F4] makes the right side ∑λ∈spec⁡(An)∩(aj,bj)∥Pλxn∥22; therefore there is an actual eigenvalue in (aj,bj).

3.1F4step 2.2algebra∎

These conclusions hold for all j on the same weak-convergence event, since its bounded-continuous-test property applies to each φj. In particular j=1 ensures at least one positive eigenvalue for every sufficiently large n, so the smallest positive eigenvalue λmin⁡+(An) is eventually defined. For every fixed j, step 2.2 gives 0<λmin⁡+(An)<bj for all sufficiently large n; since bj↓0, it follows that λmin⁡+(An)→0 on that probability-one event. This excludes an eventual uniform positive lower bound and proves the stated gap failure for the full complex entry class without claiming convergence of finite-sample reciprocal traces.

Source notes

Chapter 3 of Bai and Silverstein (full book PDF).

Theorem 1 of Bai, Miao, and Pan (full paper PDF).

Depends on

Used by

Nothing in the library uses this result yet.

Dependency tree · two levels

52 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