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.
Stabilizability and detectability for continuous and discrete linear systems
Definition
Let , , , , and . A matrix is Hurwitz when every complex eigenvalue has strictly negative real part, as in Hurwitz matrices and the continuous Lyapunov equation. It is Schur stable when every complex eigenvalue has modulus strictly less than one.
The pair is continuously stabilizable if some makes Hurwitz. The pair is continuously detectable if some makes Hurwitz.
The pair is discretely stabilizable if some makes Schur stable. The pair is discretely detectable if some makes Schur stable. These are exact existential properties of the given finite matrices. A residual size or an approximate spectral estimate does not itself establish any of them.
Depends on
Used by
- Standard CARE and DARE data, equations, and conventions Definition
- Unstabilizable and undetectable unstable modes break the CARE conclusion Example
- Powers of a Schur-stable matrix decay geometrically up to a polynomial factor Lemma
- Stabilizability and detectability imply the closed-region PBH rank tests Lemma
- Closed-region PBH rank tests characterize continuous and discrete stabilizability and detectability Theorem
- Stabilizability and detectability give the unique stabilizing positive-semidefinite CARE solution Theorem
- Stabilizability and detectability give the unique stabilizing positive-semidefinite DARE solution Theorem
Dependency tree · two levels
6 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
- Daniel Liberzon, Calculus of Variations and Optimal Control Theory: A Concise Introduction, Chapter 6 (standard reference, not scraped)
- Peter Benner and Lihong Feng, Model Reduction for Dynamical Systems, Lecture 8 (standard reference, not scraped)
- Federico Poloni, Lecture notes on Riccati-type matrix equations (standard reference, not scraped)