Alphabeta Math
AlgorithmAlgorithm: Literature-sourcedProof: Not applicablePipeline-generatedprecheck pendingjudge 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 barycentric interpolation evaluator

Definition

The inputs are a natural number n≥0, a floating-point system (F,Nmin⁡,Nmax⁡,fl⁡) satisfying The standard relative floating-point model and unit roundoff, finite arrays X and Y of real candidate nodes and data, and a real query x. No length assumption is imposed on X or Y; their intended lengths are n+1, which the procedure checks. The output is either a record with status success and a value in F, or one of the statuses invalid_nodes, invalid_data, and range_failure.

Use checked operations as follows. For a,b∈F and ∘∈{+,−,×,÷}, return range_failure if division has b=0 or if the exact real result a∘b is nonzero and outside the normal range Nmin⁡≤∣a∘b∣≤Nmax⁡. Otherwise return fl⁡(a∘b), using the exact-zero rule when the result is zero. Each checked operation has the tagged outcome either range_failure or a value in F; if an operation in a displayed sequence returns range_failure, stop before evaluating the next operation and return that status. Thus fl⁡ is called only on arguments in the domain of the stated model.

Procedure:

  1. If the node array has length other than n+1, any node is not in F, or the nodes are not strictly increasing, return invalid_nodes. If the data array has length other than n+1, or any data value or the query is not in F, return invalid_data.
  2. If x=xi for some i, return success with value yi.
  3. If 1∉F, return range_failure. Otherwise, for each j=0,…,n, initialize dj=1. For each k=0,…,n with k≠j, compute δ=checked⁡−(xj,xk) and then replace dj by checked⁡×(dj,δ). After this inner loop set wj=checked⁡÷(1,dj). Any checked-operation failure returns range_failure.
  4. Initialize N=0 and D=0. For each j=0,…,n, compute δj=checked⁡−(x,xj), uj=checked⁡÷(checked⁡×(wj,yj),δj), and vj=checked⁡÷(wj,δj). Replace N by checked⁡+(N,uj) and D by checked⁡+(D,vj). Any checked-operation failure returns range_failure.
  5. If D=0, return range_failure. Otherwise return success with value checked⁡÷(N,D), unless that final checked operation returns range_failure.

The exact-arithmetic target of steps 3–5 is the quotient in Barycentric interpolation identities. This finite-arithmetic procedure provides no forward-error bound, conditioning guarantee, or accuracy certificate; close or widely separated nodes can cause a range failure or an inaccurate successful value.

Depends on

Used by

Dependency tree · two levels

12 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