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.
Standard CARE and DARE data, equations, and conventions
Definition
Let , , , , and be positive definite. Write and . The inverse is well-defined: A matrix admits a Cholesky factorisation with positive diagonal exactly when it is Hermitian positive definite, and that factor is unique supplies an invertible factor , so . Thus and . The symbol is transpose over and conjugate transpose over .
The continuous algebraic Riccati equation (CARE) is Its residual is , its feedback is , and its closed-loop matrix is .
For , is positive definite because for every nonzero . The discrete algebraic Riccati equation (DARE) is Its residual is . Whenever , the matrix is positive definite if . Its feedback and closed-loop matrix are A CARE solution is stabilizing when it is Hermitian and is Hurwitz. A DARE solution is stabilizing when it is Hermitian, , and is Schur stable. No inverse of is part of either definition.
For Hermitian with invertible, multiplication gives In either product, the additional coefficient of is , because . Thus the DARE is equivalently , and its closed-loop matrix is .
The associated homogeneous symplectic pencil is The homogeneous-coordinate convention is that a finite eigenvalue is and denotes infinity.
Depends on
Used by
- Finite-budget CARE and DARE candidate extraction from ordered Schur and QZ forms Algorithm
- Finite-budget exact or safeguarded inexact Newton-Kleinman policy iteration Algorithm
- Finite-budget structure-preserving doubling iteration for the DARE Algorithm
- A scalar CARE has two real roots but only one stabilizing branch Example
- A singular A can give a stable DARE solution and an infinite pencil eigenvalue Example
- A tiny CARE residual is inconclusive near the stability boundary Example
- Exact Newton-Kleinman decreases the cost while an unchecked inexact step can lose stability Example
- SDA preserves PSD blocks exactly and exposes a finite-precision symmetry defect Example
- Unstabilizable and undetectable unstable modes break the CARE conclusion Example
- Finite Riccati candidate, Newton-Kleinman, and doubling wrappers return a status Lemma
- The DARE doubling recurrence composes two Riccati steps and preserves positive semidefiniteness Lemma
- An invertible graph block converts an exact stable Schur or QZ basis into a Riccati solution Proposition
- Dense Riccati work counts and numerical acceptance require equation-level diagnostics Proposition
- Riccati residual sensitivity and invariant graph conditioning retain spectral separation Proposition
- The stabilizing CARE solution is the Hamiltonian stable invariant graph Proposition
- The stabilizing DARE solution is the stable deflating graph of a regular symplectic pencil Proposition
- Verified Lyapunov residual, descent, and stability checks make an inexact Newton-Kleinman step safe Proposition
- A CARE residual and Lyapunov margin certify a unique stabilizing root in a matrix ball Theorem
- A DARE residual, Stein inverse bound, and Lyapunov margin certify a stabilizing root Theorem
- Exact Newton-Kleinman iteration preserves stability, decreases cost matrices, and converges quadratically locally 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
9 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)
- A.A. Stoorvogel and A. Saberi, The discrete algebraic Riccati equation and linear matrix inequality, Memorandum COSOR 94-16 (standard reference, not scraped)