Peano existence theorem: Difference between revisions

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In [[mathematics]], in the field of [[control theory]], the '''Sylvester equation'''  is a [[Matrix (mathematics)|matrix]] [[equation]] of the form
:<math>A X + X B = C,</math>
where <math>A,B,X,C</math> are <math>n \times n</math> matrices: <math>A,B,C</math> are given and the problem is to find <math>X</math>.
 
==Existence and uniqueness of the solutions==
Using the [[Kronecker product]] notation and the [[Vectorization (mathematics)|vectorization operator]] <math>\operatorname{vec}</math>, we can rewrite the equation in the form
:<math> (I_n \otimes A +  B^T \otimes I_n) \operatorname{vec}X = \operatorname{vec}C,</math>
where <math>I_n</math> is the <math>n \times n</math> [[identity matrix]]. In this form, the Sylvester equation can be seen as a [[linear system]] of dimension <math>n^2 \times n^2</math>.<ref> However, rewriting the equation in this form is not advised for the numerical solution since this version is costly to solve and can be [[ill-conditioned]].</ref>
 
If <math>A=ULU^{-1}</math> and <math>B^T=VMV^{-1}</math> are the [[Jordan canonical form]]s of <math>A</math> and <math>B^T</math>, and <math>\lambda_i</math> and <math>\mu_j</math> are their [[eigenvalues]], one can write
:<math>I_n \otimes A +  B^T \otimes I_n = (V\otimes U)(I_n \otimes L +  M \otimes I_n)(V \otimes U)^{-1}.</math>
Since <math>(I_n \otimes L +  M \otimes I_n)</math> is [[triangular matrix| upper triangular]] with diagonal elements <math>\lambda_i+\mu_j</math>, the matrix on the left hand side is singular if and only if there exist <math>i</math> and <math>j</math> such that <math>\lambda_i=-\mu_j</math>.
 
Therefore, we have proved that the Sylvester equation has a unique solution if and only if <math>A</math> and <math>-B</math> have no common eigenvalues.
 
==Numerical solutions==
A classical algorithm for the numerical solution of the Sylvester equation is the Bartels–Stewart algorithm, which consists of transforming <math>A</math> and <math>B</math> into [[Schur decomposition|Schur form]] by a [[QR algorithm]], and then solving the resulting triangular system via [[Triangular matrix|back-substitution]]. This algorithm, whose computational cost is [[Big O notation|O]]<math>(n^3)</math> arithmetical operations, is used, among others, by [[LAPACK]] and the <code>lyap</code> function in [[GNU Octave]]. See also the <code>syl</code> function in that language.
 
==See also==
* [[Lyapunov equation]]
* [[Algebraic Riccati equation]]
 
==References==
* J. Sylvester, Sur l’equations en matrices <math>px = xq</math>, ''[[C. R. Acad. Sc. Paris]]'', 99 (1884), pp. 67 – 71,  pp. 115 – 116.
* R. H. Bartels and G. W. Stewart, Solution of the matrix equation <math>AX +XB = C</math>, ''[[Comm. ACM]]'', 15 (1972), pp. 820 – 826.
* R. Bhatia and P. Rosenthal, How and why to solve the operator equation  <math>AX -XB = Y </math> ?, ''[[Bull. London Math. Soc.]]'', 29 (1997), pp. 1 – 21.
* S.-G. Lee and Q.-P. Vu, Simultaneous solutions of Sylvester equations and idempotent matrices separating the joint spectrum, ''Linear Algebra and its Applications'', 435 (2011), pp. 2097 – 2109.
 
==Notes==
<references/>
 
==External links==
* [http://calculator-fx.com/calculator/linear-algebra/solve-sylvester-equation Online solver for arbitrary sized matrices.]
* [http://reference.wolfram.com/mathematica/ref/LyapunovSolve.html Mathematica function to solve the Sylvester equation]
 
[[Category:Matrices]]
[[Category:Control theory]]

Revision as of 12:25, 3 February 2014

In mathematics, in the field of control theory, the Sylvester equation is a matrix equation of the form

AX+XB=C,

where A,B,X,C are n×n matrices: A,B,C are given and the problem is to find X.

Existence and uniqueness of the solutions

Using the Kronecker product notation and the vectorization operator vec, we can rewrite the equation in the form

(InA+BTIn)vecX=vecC,

where In is the n×n identity matrix. In this form, the Sylvester equation can be seen as a linear system of dimension n2×n2.[1]

If A=ULU1 and BT=VMV1 are the Jordan canonical forms of A and BT, and λi and μj are their eigenvalues, one can write

InA+BTIn=(VU)(InL+MIn)(VU)1.

Since (InL+MIn) is upper triangular with diagonal elements λi+μj, the matrix on the left hand side is singular if and only if there exist i and j such that λi=μj.

Therefore, we have proved that the Sylvester equation has a unique solution if and only if A and B have no common eigenvalues.

Numerical solutions

A classical algorithm for the numerical solution of the Sylvester equation is the Bartels–Stewart algorithm, which consists of transforming A and B into Schur form by a QR algorithm, and then solving the resulting triangular system via back-substitution. This algorithm, whose computational cost is O(n3) arithmetical operations, is used, among others, by LAPACK and the lyap function in GNU Octave. See also the syl function in that language.

See also

References

  • J. Sylvester, Sur l’equations en matrices px=xq, C. R. Acad. Sc. Paris, 99 (1884), pp. 67 – 71, pp. 115 – 116.
  • R. H. Bartels and G. W. Stewart, Solution of the matrix equation AX+XB=C, Comm. ACM, 15 (1972), pp. 820 – 826.
  • R. Bhatia and P. Rosenthal, How and why to solve the operator equation AXXB=Y ?, Bull. London Math. Soc., 29 (1997), pp. 1 – 21.
  • S.-G. Lee and Q.-P. Vu, Simultaneous solutions of Sylvester equations and idempotent matrices separating the joint spectrum, Linear Algebra and its Applications, 435 (2011), pp. 2097 – 2109.

Notes

  1. However, rewriting the equation in this form is not advised for the numerical solution since this version is costly to solve and can be ill-conditioned.