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.
The spectrum 1, 4, 9 gives tuned root factor one half
Example
Let , on , and . With and , tuned heavy-ball has , , and modal root factor at all three eigenvalues. Its endpoint characteristic polynomials are repeated-root polynomials. Gradient descent with step has modal multipliers , so its worst modal factor is .
Example references
Facts & Assumptions
Given: The diagonal matrix , objective , initial vector , and the two fixed-step update rules.
Tuned heavy-ball has the minimax root factor on positive-definite quadratics: for , , the tuned parameters are and , with modal root factor .
Heavy-ball momentum: the update is .
Gradient descent: in fixed-step mode, the update is .
Matrix quadratic forms have the expected first derivative and Hessian: for , the gradient is .
Verification
The eigenvalues of are , so , , , and . Substitution into [F1] gives and .
Taking gives , so [F4] yields . Projecting [F2]'s update onto each standard coordinate gives polynomial . For it is ; for it is , whose roots are and have modulus ; for it is . Thus each root factor is , and the endpoint roots are repeated.
By [F3], gradient descent with has multiplier on an eigenvector of eigenvalue . The multipliers at are , whose largest absolute value is .
Depends on
Used by
Nothing in the library uses this result yet.
Dependency tree · two levels
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