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.
A central path approaches the boundary and the Hessian conditioning diverges
Example
Consider subject to , , and . On the strict interior the logarithmic barrier objective at parameter is
Facts & Assumptions
Given: The strict feasible region and barrier objective above; write .
Any attained minimizer of the logarithmic barrier objective in the strict feasible region is a central point (Log-barrier central paths and primal-dual Newton residuals).
A real symmetric matrix has an orthonormal eigenbasis (Real spectral theorem: a self-adjoint endomorphism of a finite-dimensional real inner product space has an orthonormal eigenbasis); hence its extreme eigenvalues bound each unit-vector Rayleigh quotient.
Verification
The closed feasible triangle is compact, and lie in . On its strict interior, each barrier term is nonnegative; as any boundary point is approached, at least one of tends to zero and its negative logarithm tends to , while is bounded. Fix any strict-interior point. Its objective sublevel set is therefore separated from the boundary, closed in the compact triangle by continuity, and compact; the objective attains a strict-interior minimizer. Direct differentiation gives , whose quadratic form is for every nonzero , so the minimizer is unique. At the central point, the derivative is , hence and . The derivative is , so and . Consequently and as .
For , step 1.1 gives and . By [F2] applied to the coordinate unit vectors, and . With for this positive-definite Hessian, , exhibiting growing conditioning as the central path approaches the boundary.
Depends on
Used by
Nothing in the library uses this result yet.
Dependency tree · two levels
11 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
- Stephen Boyd and Lieven Vandenberghe, Convex Optimization (standard reference, not scraped)
- Hamza Fawzi, Topics in Convex Optimisation, Lecture 15 (standard reference, not scraped)