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.
Operator-norm bound for bounded complex iid matrices
Statement
On a common probability space, for each positive integer , let be a positive integer with , and let have independent identically distributed entries satisfying and almost surely for a fixed . Then there is a finite constant such that, almost surely, for all sufficiently large .
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.
The entries in each matrix are mutually independent and have the common law specified in the Statement (Identical distribution and IID families).
If , then almost surely only finitely many occur (First Borel-Cantelli lemma for events).
For an event and , whenever (Markov's inequality for random variables).
The operator norm is the maximum of over unit vectors in the domain (The operator norm is zero on the zero domain and otherwise is max_{||v||=1} ||Tv||).
Proof
If , every entry is zero almost surely and the conclusion is immediate. Assume . Regard the complex unit sphere in as the real unit sphere in . For , a maximal -separated subset is an -net; the disjoint radius- balls about its points lie in the radius- ball, so volume comparison bounds its cardinality by . If are such nets for the two unit spheres and is an complex matrix, approximating unit vectors attaining gives Indeed, the approximation error is at most .
Fix deterministic unit vectors and . Then is a sum of independent centered complex variables. Each real or imaginary summand is bounded in absolute value by . If a centered real variable is bounded by , convexity of the exponential on and give . Independence, exponential Markov inequality [F3], and optimization in therefore give because .
Choose in step 1.1. The product of the two net cardinalities is at most . Taking with large enough that , the union bound and step 2.1 make the probability that the net maximum exceeds at most . These probabilities are summable in because . Since all are defined on the common probability space in the Statement, [F2] applies to this sequence of events and the net maximum is at most eventually almost surely. The net inequality in step 1.1 and boundedness of then yield eventually, with deterministic .
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
Used by
Dependency tree · two levels
11 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
- Z. D. Bai and J. W. Silverstein, Spectral Analysis of Large Dimensional Random Matrices, 2nd ed. (standard reference, not scraped)