Alphabeta Math
DefinitionDefinition: AI-adaptedProof: Not applicablePipeline-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.

Finite measurable squared-error quantizer and nearest-center rule

Definition

Let d,k≥1, let X⊆Rd be Borel, and give X the relative Borel sigma algebra B(X)={X∩B:B is in the sigma algebra generated by Euclidean open sets in Rd}. Let μ be a probability measure on B(X) with ∫X∥x∥22 dμ(x)<∞, and let c1,…,ck∈Rd be reproduction points, where ∥x∥22:=∑ℓ=1dxℓ2. A finite squared-error quantizer is a measurable map Q:X→{1,…,k}, with the finite index set given its discrete sigma algebra; its reproduction at x is cQ(x) and its distortion is

Dμ(Q;c1,…,ck):=∫X∥x−cQ(x)∥22 dμ(x).

This is the expected single-letter distortion of Single-letter and block distortion for the measurable distortion d(x,c)=∥x−c∥22. For a fixed ordered center tuple c=(c1,…,ck), the nearest-center rule assigns x to the least index minimizing ∥x−cj∥22. Its cell at index i is

Ci(c):=X∩⋂j<i{x:∥x−ci∥22<∥x−cj∥22}∩⋂j>i{x:∥x−ci∥22≤∥x−cj∥22}.

The convention assigns every tie to the smallest index. A general finite measurable quantizer need not use this nearest-center partition; in particular, a compander partition below is specified by its own interval boundaries. When X=Rd, this is the ordinary full-domain nearest-center quantizer.

Depends on

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