Alphabeta Math
AlgorithmAlgorithm: Literature-sourcedProof: 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.

Nested-basis H² matrix-vector multiplication

Definition

Input: An H² representation A~ with row bases Uτ, column bases Vσ, transfer matrices from Nested H² cluster bases and interaction lists, and a vector x∈FJ.

Procedure:

  1. Initialize an upward coefficient vector zσ for each column cluster. At each leaf σ, set zσ=Vσ∗x∣σ. At each internal cluster σ with children σ1,σ2, set zσ=∑j=12(EσjV)∗zσj.
  2. Initialize a downward coefficient vector wτ=0 for each row cluster and an output vector y=0. For each admissible leaf (τ,σ), add Sτσzσ to wτ. For each near leaf (τ,σ), add Dτσx∣σ to y∣τ.
  3. Traverse the row tree from root to leaves. For each parent τ and child τi, add EτiUwτ to wτi. At each row leaf τ, add Uτwτ to y∣τ. Return y.

The upward and downward traversals follow the finite cluster trees; block and cluster contributions are accumulated in exact arithmetic.

Depends on

Used by

Dependency tree · two levels

4 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