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.
Positive definite, negative definite, semidefinite, and indefinite quadratic forms
Definition
For a real matrix , write using The Euclidean inner product on . It is positive definite when for every , negative definite when for every , positive semidefinite when for every , negative semidefinite when for every , and indefinite when it takes both positive and negative values. For a twice differentiable scalar field, its Hessian matrix is the matrix of The Hessian matrix and critical points of a scalar field.
Depends on
Used by
- An everywhere-positive-definite Hessian implies strict convexity Corollary
- A positive-semidefinite Hessian need not give strict convexity Counterexample
- A positive-definite quadratic ellipsoid is a regular level set Example
- A definite quadratic form has a uniform signed bound on the Euclidean unit sphere Lemma
- A C² function is convex exactly when its Hessian is positive semidefinite Theorem
Dependency tree · two levels
18 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
- Analysis, Convexity, and Optimization (standard reference, not scraped)