Alphabeta Math
LemmaStatement: AI-adaptedProof: AI-adaptedPipeline-generated
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.

Termination and separate algebraic resource account for rational tensor verification

Statement

For an input of Finite exact rational electrovac tensor verification with Nin input coefficient/exponent records, suppose the expanded fixed schedule has K polynomial operations and every intermediate polynomial support has at most R monomials. The algorithm terminates. Under elementary rational-coefficient arithmetic, sparse addition, multiplication, differentiation and sorted-list insertion perform at most K(2R2+2R) coefficient operations and at most that many ordered exponent-vector insertions; sorted-list insertion uses at most R support comparisons per insertion. If every intermediate support is retained, memory is at most (K+Nin)R coefficient/exponent records. These are algebraic-operation and record bounds in terms of actual intermediate support; the statement gives no polynomial input-size bound, bit-complexity bound, practical runtime estimate or optimality claim.

Facts & Assumptions

Given: The fixed finite instruction schedule and finite sparse inputs of Finite exact rational electrovac tensor verification.

[F1]

Finite exact rational electrovac tensor verification: every loop has a fixed finite index range; polynomial operations enumerate finite supports; no instruction tests convergence or iterates until a numerical tolerance is reached.

[A1]

Nin, K and R are finite nonnegative integers, with R bounding the number of monomials in every stored intermediate polynomial.

Proof

1.1givenF1algebra

The determinant uses 4!=24 signed permutation products, the inverse table has sixteen entries, the four Maxwell residuals have four coordinate terms each, and every Christoffel, Ricci and contraction table has a fixed finite index range in {0,1,2,3}. By [F1], all loops therefore make finitely many calls to arithmetic operations on finite-support polynomials.

2.1A1step 1.1algebra

A sparse sum combines at most 2R terms. A sparse product examines at most R2 ordered monomial pairs; differentiating a support of size at most R visits at most R terms. Thus for each of the K polynomial operations the stated upper bound 2R2+2R dominates the coefficient work and insertion count. A sorted support list has at most R entries, so each insertion makes at most R comparisons. Summing over K operations and retaining at most R records for each of the K intermediate results and Nin inputs gives the stated operation, insertion and memory bounds.

3.1F1step 1.1step 2.1∎

All integer and rational coefficient operations and all finite list traversals terminate; the schedule has no convergence or data-dependent loop. The finite operation schedule from step 1.1 therefore terminates. The bounds count algebraic operations and records only; they do not bound coefficient bit lengths or elapsed machine time.

Remarks

The script execution time stored with a finite check is an observation about that execution, not a consequence of this lemma.

Depends on

Dependency tree · 2 levels

Sources