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The Hurwitz Lyapunov integral is the unique solution and has the stated positivity
Statement
Let be Hurwitz and let . Then the improper matrix integral
converges and is the unique solution of . If , then . If , then . For , if and only if there is no nonzero for which for every .
Facts & Assumptions
Given: A Hurwitz matrix , a self-adjoint matrix , and the definitions of and from Hurwitz matrices and the continuous Lyapunov equation.
The exponential series of a bounded operator: , and the same formula applies to .
A non-negative operator has a unique non-negative square root: A non-negative finite-dimensional self-adjoint operator has a unique non-negative square root with .
The Frobenius norm on real or complex matrices: is the sum of squared absolute values of the entries of .
Every linear map between finite-dimensional real or complex inner product spaces admits a singular value decomposition: A finite-dimensional matrix has a singular value decomposition, and its adjoint has the same positive singular values.
The operator norm is 0 on the zero domain and otherwise equals the largest singular value, attained at a right-singular vector: The operator norm of a finite-dimensional map is its largest singular value.
Proof
Put . For finite matrices , by the operator-norm definition and [F4]. The entry formula in [F4] gives . The SVD in [F5] gives the same positive singular values for and ; [F6] therefore gives . Applying the left-multiplication estimate to adjoints yields . Applying these inequalities successively gives .
For , [F2] and the product rule give . Integrating this identity yields .
By [F1], the right side in step 1.1 is bounded by a finite sum of polynomial multiples of . Each such term is integrable on by repeated integration by parts. Every entry of the integrand is therefore absolutely integrable; define by the finitely many scalar improper entry integrals. The entrywise tails tend to zero, so in Frobenius norm and the matrix integral converges absolutely in that norm. The integrand is self-adjoint because .
By [F1], as , and by step 2.1, . Taking limits in step 1.2 gives .
Suppose . By [F3], exists and is self-adjoint, so . Hence for every , . Thus . If and , the integrand at is and is continuous, so it stays positive on an interval of positive length; therefore and .
If is another solution, satisfies . The derivative of is therefore zero by [F2], so it is constant and equals at . By [F1] it tends to zero as , hence and the solution is unique.
For and nonzero , the continuous integrand in step 3.2 has integral zero exactly when it vanishes for all : if it were positive at one time, continuity would make its integral over a neighboring interval positive. It vanishes at time exactly when . Hence for every nonzero exactly when no nonzero trajectory remains in that kernel for all .
Depends on
- Hurwitz matrices and the continuous Lyapunov equation
- A Hurwitz matrix exponential has a polynomial-exponential decay bound
- The exponential series of a bounded operator
- A non-negative operator has a unique non-negative square root
- The Frobenius norm $\lVert A\rVert_F=(\sum_{i,j}|a_{ij}|^2)^{1/2}$ on real or complex matrices
- The operator norm is zero on the zero domain and otherwise is max_{||v||=1} ||Tv||
- Every linear map between finite-dimensional real or complex inner product spaces admits a singular value decomposition
- The operator norm is 0 on the zero domain and otherwise equals the largest singular value, attained at a right-singular vector
Used by
- Finite-budget exact or safeguarded inexact Newton-Kleinman policy iteration Algorithm
- A stable linear noise covariance balance has a positive solution Example
- Riccati residual sensitivity and invariant graph conditioning retain spectral separation 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 proved rational shift bound implies low-rank ADI convergence Theorem
- Exact Newton-Kleinman iteration preserves stability, decreases cost matrices, and converges quadratically locally Theorem
Dependency tree · two levels
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Sources
- Valeria Simoncini, Computational Methods for Linear Matrix Equations, SIAM Review 58(3), 2016 (standard reference, not scraped)
- Elias Jarlebring, Numerical Methods for Lyapunov Equations, lecture notes in numerical linear algebra, 2017 (standard reference, not scraped)