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✓ 6 results · all verified · 4 also independently AI-judged
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Numerical algebraic Riccati equations and stabilizing branches — Examples

1 · Prerequisites

2 · Summary

Examples

These examples check the scalar CARE branch, a singular-state DARE pencil with an infinite eigenvalue, the effect of near-boundary conditioning on residual certification, and failures when stabilizability or detectability is absent. The iteration examples contrast exact Newton-Kleinman descent with an unchecked inexact step and show how exact SDA PSD invariants differ from a rounded block with a measurable symmetry defect.

3 · Logical flowchart

4 · Definitions, theorems and proofs

None yet.

5 · Examples, counterexamples and false statements

ExampleConstruction: Literature-sourcedVerification: AI-adaptedprecheck passjudge pass (gpt-6-luna)Open item page →

A scalar CARE has two real roots but only one stabilizing branch

Example

Take the scalar continuous data A=B=C=R=1. Then Q=1 and G=1.

Facts & Assumptions

Given: The scalar data above and the CARE convention of Standard CARE and DARE data, equations, and conventions.

[F1]

Standard CARE and DARE data, equations, and conventions: the CARE residual is A∗X+XA−XGX+Q, and a CARE root is stabilizing when A−GX is Hurwitz.

Proof

1.1F1givenalgebra

Substitution gives 2X−X2+1=0, or (X−1)2=2. The two real roots are X+=1+2 and X−=1−2.

2.1F1step 1.1algebra∎

The corresponding closed-loop scalars are 1−X+=−2<0 and 1−X−=2>0. Thus X+ is the only stabilizing branch; it is positive, whereas the other root is negative and unstable.

ExampleConstruction: Literature-sourcedVerification: AI-adaptedprecheck passjudge pass (gpt-6-luna)Open item page →

A singular A can give a stable DARE solution and an infinite pencil eigenvalue

Example

Take scalar A=B=0, C=R=1. Then Q=1 and the state matrix A is singular.

Facts & Assumptions

Given: The data above and the homogeneous pencil convention of Standard CARE and DARE data, equations, and conventions.

[F1]

Standard CARE and DARE data, equations, and conventions: the DARE is X=Q+A∗XA−A∗XB(R+B∗XB)−1B∗XA, its closed loop is Ac=A−B(R+B∗XB)−1B∗XA, and its homogeneous pencil is βN−αM with finite coordinate [λ:1] and infinity [1:0].

Proof

1.1F1givenalgebra

Here G=0 and R+B∗XB=1. The DARE reduces to X=1, so its solution is X=1 and Ac=0, which is Schur stable.

2.1F1step 1.1algebra∎

The pencil matrices are N=[00−11] and M=[1000]. Therefore det⁡(βN−αM)=det⁡[−α0−ββ]=−αβ. This nonzero homogeneous polynomial has roots [0:1] (the finite eigenvalue 0) and [1:0] (an infinite eigenvalue). No inverse of A is used.

ExampleConstruction: Literature-sourcedVerification: AI-adaptedprecheck passjudge pass (gpt-6-luna)Open item page →

Unstabilizable and undetectable unstable modes break the CARE conclusion

Example

Two scalar cases isolate the roles of continuous stabilizability and detectability for the CARE conclusion.

Facts & Assumptions

[F1]

Stabilizability and detectability for continuous and discrete linear systems: stabilizability/detectability mean that some feedback/output injection makes the corresponding matrix Hurwitz.

[F2]

Standard CARE and DARE data, equations, and conventions: the scalar CARE residual is 2AX−GX2+Q and the closed loop is A−GX.

[F3]

Stabilizability and detectability give the unique stabilizing positive-semidefinite CARE solution: under both hypotheses the CARE has exactly one positive-semidefinite solution and its closed loop is Hurwitz.

Proof

1.1F1F2givenalgebra

First take A=C=R=1 and B=0. Then Q=1; every feedback leaves A−BK=1, so the pair is not stabilizable. The CARE is 2X+1=0, whose only root is X=−1/2; it is not positive semidefinite and its closed loop remains 1.

2.1F1F2F3step 1.1algebra∎

Next take A=B=R=1 and C=0, so Q=0. The pair is stabilizable, since K=2 gives A−BK=−1, but it is not detectable because A−LC=1 for every L. The CARE is 2X−X2=0, with positive-semidefinite roots X=0 and X=2; the first has unstable closed loop 1 and the second stable closed loop −1. Since Q=0, zero control has zero cost for every initial state, so the value is zero rather than the stabilizing root X=2. Thus without detectability uniqueness among PSD roots and the stabilizing value conclusion in [F3] fail.

ExampleConstruction: Literature-sourcedVerification: AI-adaptedprecheck passjudge pass (gpt-6-luna)Open item page →

A tiny CARE residual is inconclusive near the stability boundary

Example

Let ε=10−8 and take scalar CARE data A=ε, B=R=1, C=ε, hence Q=ε2 and G=1. Use the candidate X^=1.1ε.

Facts & Assumptions

Given: The scalar data above and the CARE residual-ball criterion of A CARE residual and Lyapunov margin certify a unique stabilizing root in a matrix ball.

[F1]

Standard CARE and DARE data, equations, and conventions: Fc(X)=A∗X+XA−XGX+Q and Ac(X)=A−GX.

[F2]

A CARE residual and Lyapunov margin certify a unique stabilizing root in a matrix ball: a radius must satisfy κ(r+gρ2)≤ρ and 2κgρ<1 for its fixed-point certificate.

Proof

1.1F1givenalgebra

The scalar CARE is 2εX−X2+ε2=0, with positive root X∗=(1+2)ε and stable closed loop Ac(X∗)=−2ε. At the candidate, A^c=−0.1ε<0, the exact residual is r=∣Fc(X^)∣=1.99ε2=1.99⋅10−16, and g=1.

2.1F2step 1.1algebra∎

The scalar Lyapunov derivative is L(E)=2A^cE=−0.2εE, so its inverse norm is κ=1/(0.2ε)=5⋅108. For every ρ>0, r+ρ2≥2r ρ, so κ(r+ρ2)≥2κr ρ>ρ because 2κr>14. Thus no radius satisfies even the self-map condition, despite the absolute residual being below 2⋅10−16 and the exact stabilizing root existing. The residual alone is inconclusive because the inverse Lyapunov factor is large near the imaginary axis.

ExampleConstruction: Literature-sourcedVerification: AI-adaptedprecheck passOpen item page →

Exact Newton-Kleinman decreases the cost while an unchecked inexact step can lose stability

Example

Take scalar CARE data A=B=C=R=1 and the stabilizing starting gain K0=2.

Facts & Assumptions

[F1]

Finite-budget exact or safeguarded inexact Newton-Kleinman policy iteration: exact mode solves (A−BKj)∗Xj+Xj(A−BKj)=−(Q+Kj∗RKj) and then sets Kj+1=R−1B∗Xj.

[F2]

Finite-budget exact or safeguarded inexact Newton-Kleinman policy iteration: inexact line-search trials are Kj+θℓ(K~j−Kj) for the finite list θℓ=2−ℓ.

[F3]

Verified Lyapunov residual, descent, and stability checks make an inexact Newton-Kleinman step safe: an accepted inexact trial must have a certified Hurwitz closed loop and an exact cost matrix no greater than the current one.

Proof

1.1F1givenalgebra

At K0=2, the closed loop is −1 and the exact Lyapunov equation gives −2X0+5=0, hence X0=5/2 and K1=5/2. The next closed loop is −3/2, and its policy equation gives −3X1+1+25/4=0, hence X1=29/12<5/2.

2.1F2F3step 1.1algebra∎

Consider instead an unchecked approximate policy value P~=0, which proposes K~=0. A full step gives closed loop A−BK~=1, which is unstable. More generally, a line-search trial between K0=2 and 0 has K∈[0,2]. If K≤1 its closed loop 1−K is not Hurwitz. If 1<K<2, its exact cost matrix is P(K)=(1+K2)/(2(K−1)), and P(K)−52=(K−2)(K−3)2(K−1)>0. Thus every nonzero trial either loses stability or increases cost; the certified safeguard rejects it.

ExampleConstruction: Literature-sourcedVerification: AI-adaptedprecheck passOpen item page →

SDA preserves PSD blocks exactly and exposes a finite-precision symmetry defect

Example

For one exact SDA step, take A=I2, G0=diag⁡(1,0), and H0=[1111].

Facts & Assumptions

Given: The matrices above and the recurrence in Finite-budget structure-preserving doubling iteration for the DARE.

[F1]

Finite-budget structure-preserving doubling iteration for the DARE: W=(I+GH)−1, G1=G+AWGA∗, and H1=H+A∗HWA.

[F2]

The DARE doubling recurrence composes two Riccati steps and preserves positive semidefiniteness: when G,H⪰0, the exact inverse exists and the updated G1,H1 remain positive semidefinite.

Proof

1.1F1F2givenalgebra

The inputs are positive semidefinite, and I+G0H0=[2101],W0=[12−1201]. Using [F1] gives A1=W0,G1=[32000],H1=[32323232]. Both updated symmetric blocks are positive semidefinite; G1 is diagonal PSD and H1=32(1,1)T(1,1).

2.1F1F2step 1.1algebra∎

To illustrate the diagnostic, perturb only one stored off-diagonal entry by ϵ>0 and report H~1=[3232+ϵ3232]. Its symmetry defect is ∥H~1−H~1∗∥F=2 ϵ. Even its symmetrization has determinant 94−(32+ϵ2)2<0, so this perturbed stored block is not positive semidefinite. It must be reported and rechecked rather than treated as covered by the exact-arithmetic PSD invariant.

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