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 damped least-squares step solves a scaled trust-region model
Statement
Let , , , let be nonsingular, let , and put . Assume . There is a unique satisfying Set , , and . Then uniquely minimizes over , and globally minimizes the unregularized model over the trust-region ball of Quadratic trust-region models and the actual-to-predicted reduction ratio. Its predicted reduction is positive and, in the original step coordinates,
Facts & Assumptions
Given: The matrices and vector in the Statement, with nonsingular, , and .
Quadratic trust-region models and the actual-to-predicted reduction ratio: a quadratic model is minimized over a Euclidean ball; its predicted reduction is , and an actual-to-predicted ratio is defined only when that reduction is positive.
Positive-definite Newton and dogleg steps are well-defined: for a real symmetric positive-definite model matrix , the system has a unique solution.
The Euclidean inner product on : the Euclidean inner product is symmetric and bilinear, and .
Rectangular matrix multiplication and the identity matrix , including zero-sized shapes: a compatible product is defined entrywise by finite sums, and is the identity matrix.
Proof
For every nonzero , because is nonsingular. The matrix is symmetric positive definite, so [F2] gives a unique . With , the system is equivalent, after left multiplication by , to . Expanding and completing the square around its unique critical point shows , with equality only at .
If , minimality from step 1.1 gives . Subtracting the common penalty proves for every feasible , so globally minimizes on the stated ball. Expanding shows its linear coefficient is and its symmetric quadratic coefficient is , so it is the trust-region model of [F1].
The damped system gives . Expanding yields the displayed formula. Since , invertibility of the damped matrix gives , and nonsingularity of makes the displayed reduction strictly positive. Thus the ratio denominator is valid.
Depends on
- The Euclidean inner product $\langle x,y\rangle = \sum_{k<n} x_k y_k$ on $\mathbb{R}^n$
- Rectangular matrix multiplication and the identity matrix $I_n$, including zero-sized shapes
- Quadratic trust-region models and the actual-to-predicted reduction ratio
- Positive-definite Newton and dogleg steps are well-defined
Used by
- Finite-budget Levenberg-Marquardt with a trust-ratio safeguard Algorithm
- Rank-deficient data do not identify both parameters, while LM has a unique damped step Example
- Uniformly large damping forces an acceptable LM trial in finitely many attempts Lemma
- Exact safeguarded Levenberg-Marquardt iterates approach stationarity on compact sublevels Theorem
Dependency tree · two levels
29 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
- Jorge Nocedal and Stephen J. Wright, Numerical Optimization, second edition (standard reference, not scraped)
- David Bindel, CS 4220: Numerical Analysis, Gauss-Newton and nonlinear least squares (standard reference, not scraped)