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Operator-norm bound for bounded complex iid matrices

Statement

On a common probability space, for each positive integer n, let Nn be a positive integer with n/Nn→c∈(0,∞), and let Xn∈Cn×Nn have independent identically distributed entries ξ satisfying Eξ=0 and ∣ξ∣≤K almost surely for a fixed K<∞. Then there is a finite constant C=C(K,c) such that, almost surely, ∥Xn∥op≤CNn for all sufficiently large n.

Statement references

Bai and Silverstein, Chapter 3, full book PDF (source).

Facts & Assumptions

Given: The matrix sequence on its common probability space and its bounded centered iid entry law from the Statement.

[F1]

The entries in each matrix are mutually independent and have the common law specified in the Statement (Identical distribution and IID families).

[F2]

If ∑nP(En)<∞, then almost surely only finitely many En occur (First Borel-Cantelli lemma for events).

[F3]

For an event E and a>0, P(E)≤E[Y]/a whenever Y≥a1E (Markov's inequality for random variables).

[F4]

The operator norm is the maximum of ∥Av∥2 over unit vectors in the domain (The operator norm is zero on the zero domain and otherwise is max_{||v||=1} ||Tv||).

Proof

1.1givenF4algebra

If K=0, every entry is zero almost surely and the conclusion is immediate. Assume K>0. Regard the complex unit sphere in Cd as the real unit sphere in R2d. For 0<ε<1/2, a maximal ε-separated subset is an ε-net; the disjoint radius-ε/2 balls about its points lie in the radius-(1+ε/2) ball, so volume comparison bounds its cardinality by (1+2/ε)2d. If N1,N2 are such nets for the two unit spheres and A is an n×N complex matrix, approximating unit vectors u,v attaining ∥A∥op=∣u∗Av∣ gives ∥A∥op≤(1−2ε)−1max⁡u0∈N1, v0∈N2∣u0∗Av0∣. Indeed, the approximation error is at most ∥u−u0∥∥A∥+∥v−v0∥∥A∥≤2ε∥A∥.

2.1givenF1F3step 1.1algebra

Fix deterministic unit vectors u∈Cn and v∈CN. Then u∗Xnv=∑i,ju‾ivjξij is a sum of independent centered complex variables. Each real or imaginary summand is bounded in absolute value by K∣ui∣∣vj∣. If a centered real variable Y is bounded by b, convexity of the exponential on [−b,b] and EY=0 give EeλY≤cosh⁡(λb)≤eλ2b2/2. Independence, exponential Markov inequality [F3], and optimization in λ therefore give P{∣u∗Xnv∣>t}≤4exp⁡ ⁣(−t24K2), because ∑i,jK2∣ui∣2∣vj∣2=K2.

3.1givenF2step 1.1step 2.1algebra∎

Choose ε=1/8 in step 1.1. The product of the two net cardinalities is at most 172(n+Nn). Taking t=Ln+Nn with L large enough that L2/(4K2)>2log⁡17+1, the union bound and step 2.1 make the probability that the net maximum exceeds t at most 4e−(n+Nn). These probabilities are summable in n because Nn/n→1/c>0. Since all Xn are defined on the common probability space in the Statement, [F2] applies to this sequence of events and the net maximum is at most Ln+Nn eventually almost surely. The net inequality in step 1.1 and boundedness of n/Nn then yield ∥Xn∥op≤CNn eventually, with deterministic C=C(K,c).

Source notes

The proof is the explicit finite-net and exponential-moment argument above; the sources record the standard norm-estimation context and are not imported as proof premises.

Depends on

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Sources