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Numerical algebraic Riccati equations and stabilizing branches — Examples
1 · Prerequisites
- Absolute and Conditional Convergence; Rearrangement; Products
- Algebraic Closure, Embeddings, and Separability
- Algebraic Extensions, Extension Degree, and Finite Fields
- Binary Operations, Monoids, Groups and Subgroups
- Bounded Linear Operators and Quotient Spaces
- Compactness in Metric Spaces
- Completeness, Completion, and Uniform Continuity
- Complex Norms and Realification
- Complexification, Realification and Real Structures
- Composition Series, the Jordan–Hölder Theorem and Solvable Groups
- Computed Schur decompositions and nonnormal spectra
- Computed singular values and rank revealing factorizations
- Congruences, the Integers Modulo n and the Chinese Remainder Theorem
- Connectedness
- Construction of the Natural Numbers
- Construction of the Real Numbers via Cauchy Sequences
- Construction of the Real Numbers via Dedekind Cuts
- Continuity, IVT, EVT, and Uniform Continuity
- Cosets, Index and Lagrange's Theorem
- Countability and Uncountability
- Cyclic Groups and Direct Products
- Determinants of Matrices over a Commutative Ring
- Diagonalisation and the Minimal Polynomial
- Direct Matrix Factorisations: LU, Cholesky and QR
- Divisibility, Euclidean Domains, Principal Ideal Domains and Unique Factorisation
- Divisibility, Greatest Common Divisors and Bézout's Identity
- Dual Spaces, Bilinear and Quadratic Forms, and Sylvester's Law of Inertia
- Eigenvalue Iterations and the QR Algorithm
- Eigenvalues, Eigenvectors and the Characteristic Polynomial
- Euclidean Ordinary Differential Equations with Smooth Dependence
- Filters and Ultrafilters
- Finite Counting, Factorials and Binomial Coefficients
- Finite Dimensional Normed Spaces and Riesz Lemma
- Finite-precision direct factorizations and least squares
- Foundations of the Real Numbers for Analysis
- Group Actions, Orbits, Stabilisers and Cayley's Theorem
- Group Homomorphisms and the Isomorphism Theorems
- Hilbert Space Geometry and Riesz Representation
- Ideals, Quotient Rings and the Isomorphism Theorems for Rings
- Inner Product Spaces, Gram-Schmidt, Projections and Adjoints
- Limits of Real Functions
- limsup, liminf, and Subsequential Limits
- Linear Independence, Bases and Dimension
- Linear Transformations, Rank-Nullity and Quotient Spaces
- Matrices, the Matrix of a Linear Map, and Change of Basis
- Matrix Norms, Condition Numbers and Numerical Stability
- Metric Spaces
- Monotone Functions, Discontinuities, and Continuity Sets
- Monotone Sequences, Bolzano-Weierstrass, and Cauchy Completeness
- Normal Subgroups and Quotient Groups
- Normed and Banach Spaces
- Numerical algebraic Riccati equations and stabilizing branches
- Order, Zorn's Lemma, and the Axiom of Choice
- Outward rounded interval and ball arithmetic
- Picard-Lindelöf and First-Order Ordinary Differential Equations
- Polynomial Rings, the Division Algorithm and Roots
- Power Series and Real-Analytic Functions
- Primes, Euclid's Lemma and the Fundamental Theorem of Arithmetic
- Properties of the Integral and the Working FTC
- Relations, Functions, and Quotients
- Rings, Subrings, Integral Domains and Fields
- Rⁿ as a Normed Space; Vector-Valued Functions
- Roots, Rational Powers, and Classical Inequalities
- Sequences and Limits
- Sequences and Series of Functions; Uniform Convergence
- Series: Convergence and the Nonnegative Tests
- Simple Field Extensions and the Construction of the Complex Numbers
- Splitting Fields
- Strongly Continuous Semigroups and Hille Yosida
- Subspaces, Products, and Quotients
- Suprema and Infima
- Sylow's Theorems, p-Groups and Nilpotent Groups
- Sylvester and lyapunov matrix equations
- Symmetric Groups, Cycle Decomposition and the Sign Homomorphism
- The Derivative and the Mean Value Theorems
- The Determinant of a Linear Operator, Cofactors and Cramer's Rule
- The Exponential Function
- The Fundamental Theorem of Algebra
- The Fundamental Theorem of Finite Abelian Groups
- The Galois Correspondence
- The Riemann Integral: Definition and Integrability
- The Spectral Theorem, Positive Operators and Singular Value Decomposition
- The ZFC Axioms and the Basic Set Constructions
- Topological Spaces and Continuity
- Topology of ℝ
- Triangularisation, Generalised Eigenspaces and Jordan Canonical Form
- Vector Spaces, Linear Subspaces, Span and Direct Sums
- Verified linear systems and spectral enclosures
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
A scalar CARE has two real roots but only one stabilizing branch
Example
Take the scalar continuous data . Then and .
Facts & Assumptions
Given: The scalar data above and the CARE convention of Standard CARE and DARE data, equations, and conventions.
Standard CARE and DARE data, equations, and conventions: the CARE residual is , and a CARE root is stabilizing when is Hurwitz.
Proof
Substitution gives , or . The two real roots are and .
The corresponding closed-loop scalars are and . Thus is the only stabilizing branch; it is positive, whereas the other root is negative and unstable.
A singular A can give a stable DARE solution and an infinite pencil eigenvalue
Example
Take scalar , . Then and the state matrix is singular.
Facts & Assumptions
Given: The data above and the homogeneous pencil convention of Standard CARE and DARE data, equations, and conventions.
Standard CARE and DARE data, equations, and conventions: the DARE is , its closed loop is , and its homogeneous pencil is with finite coordinate and infinity .
Proof
Here and . The DARE reduces to , so its solution is and , which is Schur stable.
The pencil matrices are and . Therefore This nonzero homogeneous polynomial has roots (the finite eigenvalue ) and (an infinite eigenvalue). No inverse of is used.
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
Given: The continuous definitions and CARE theorem of Stabilizability and detectability for continuous and discrete linear systems, Standard CARE and DARE data, equations, and conventions, and Stabilizability and detectability give the unique stabilizing positive-semidefinite CARE solution.
Stabilizability and detectability for continuous and discrete linear systems: stabilizability/detectability mean that some feedback/output injection makes the corresponding matrix Hurwitz.
Standard CARE and DARE data, equations, and conventions: the scalar CARE residual is and the closed loop is .
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
First take and . Then ; every feedback leaves , so the pair is not stabilizable. The CARE is , whose only root is ; it is not positive semidefinite and its closed loop remains .
Next take and , so . The pair is stabilizable, since gives , but it is not detectable because for every . The CARE is , with positive-semidefinite roots and ; the first has unstable closed loop and the second stable closed loop . Since , zero control has zero cost for every initial state, so the value is zero rather than the stabilizing root . Thus without detectability uniqueness among PSD roots and the stabilizing value conclusion in [F3] fail.
A tiny CARE residual is inconclusive near the stability boundary
Example
Let and take scalar CARE data , , , hence and . Use the candidate .
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.
A CARE residual and Lyapunov margin certify a unique stabilizing root in a matrix ball: a radius must satisfy and for its fixed-point certificate.
Proof
The scalar CARE is , with positive root and stable closed loop . At the candidate, , the exact residual is , and .
The scalar Lyapunov derivative is , so its inverse norm is . For every , , so because . Thus no radius satisfies even the self-map condition, despite the absolute residual being below and the exact stabilizing root existing. The residual alone is inconclusive because the inverse Lyapunov factor is large near the imaginary axis.
Exact Newton-Kleinman decreases the cost while an unchecked inexact step can lose stability
Example
Take scalar CARE data and the stabilizing starting gain .
Facts & Assumptions
Given: The data above and the exact/inexact iteration conventions of Finite-budget exact or safeguarded inexact Newton-Kleinman policy iteration and Verified Lyapunov residual, descent, and stability checks make an inexact Newton-Kleinman step safe.
Finite-budget exact or safeguarded inexact Newton-Kleinman policy iteration: exact mode solves and then sets .
Finite-budget exact or safeguarded inexact Newton-Kleinman policy iteration: inexact line-search trials are for the finite list .
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
At , the closed loop is and the exact Lyapunov equation gives , hence and . The next closed loop is , and its policy equation gives , hence .
Consider instead an unchecked approximate policy value , which proposes . A full step gives closed loop , which is unstable. More generally, a line-search trial between and has . If its closed loop is not Hurwitz. If , its exact cost matrix is , and Thus every nonzero trial either loses stability or increases cost; the certified safeguard rejects it.
SDA preserves PSD blocks exactly and exposes a finite-precision symmetry defect
Example
For one exact SDA step, take , , and .
Facts & Assumptions
Given: The matrices above and the recurrence in Finite-budget structure-preserving doubling iteration for the DARE.
The DARE doubling recurrence composes two Riccati steps and preserves positive semidefiniteness: when , the exact inverse exists and the updated remain positive semidefinite.
Proof
The inputs are positive semidefinite, and Using [F1] gives Both updated symmetric blocks are positive semidefinite; is diagonal PSD and .
To illustrate the diagnostic, perturb only one stored off-diagonal entry by and report . Its symmetry defect is Even its symmetrization has determinant , 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.
Sources
- Daniel Liberzon, Calculus of Variations and Optimal Control Theory: A Concise Introduction, Chapter 6
- A.A. Stoorvogel and A. Saberi, The discrete algebraic Riccati equation and linear matrix inequality, Memorandum COSOR 94-16
- Peter Benner and Lihong Feng, Model Reduction for Dynamical Systems, Lecture 8
- Federico Poloni, Lecture notes on Riccati-type matrix equations