In mathematics, in the field of control theory, a Sylvester equation is a matrix equation of the form: Then given matrices A, B, and C, the problem is to find the possible matrices X that obey this equation. All matrices are assumed to have coefficients in the complex numbers. For the equation to make sense, the matrices must have appropriate sizes, for example they could all be square matrices of the same size. But more generally, A and B must be square matrices of sizes n and m respectively, and then X and C both have n rows and m columns. A Sylvester equation has a unique solution for X exactly when there are no common eigenvalues of A and −B. More generally, the equation AX + XB = C has been considered as an equation of bounded operators on a Banach space. In this case, the condition for the uniqueness of a solution X is almost the same: There exists a unique solution X exactly when the spectra of A and −B are disjoint.
Existence and uniqueness of the solutions
Using the Kronecker product notation and the vectorization operator, we can rewrite Sylvester's equation in the form where is of dimension, is of dimension, of dimension and is the identity matrix. In this form, the equation can be seen as a linear system of dimension. Proposition. Given complex matrices and, Sylvester's equation has a unique solution for all if and only if and have no common eigenvalues. Proof. Consider the linear transformation given by. Suppose that and have no common eigenvalues. Then their characteristic polynomials and have highest common factor. Hence there exist complex polynomials and such that. By the Cayley–Hamilton theorem, ; hence. Let be any solution of ; so and repeating this one sees that. Hence by the rank plus nullity theorem is invertible, so for all there exists a unique solution. Conversely, suppose that is a common eigenvalue of and. Note that is also an eigenvalue of the transpose. Then there exist non-zero vectors and such that and. Choose such that, the vector whose entries are the complex conjugates of. Then has no solution, as is clear from the complex bilinear pairing ; the right-hand side is positive whereas the left is zero.
Roth's removal rule
Given two square complex matrices A and B, of size n and m, and a matrixC of size n by m, then one can ask when the following two square matrices of size n + m are similar to each other: and. The answer is that these two matrices are similar exactly when there exists a matrix X such that AX − XB = C. In other words, X is a solution to a Sylvester equation. This is known as Roth's removal rule. One easily checks one direction: If AX − XB = C then Roth's removal rule does not generalize to infinite-dimensional bounded operators on a Banach space.
