An algorithm for polynomial matrix SVD based on generalised Kogbetliantz transformations

An algorithm is presented for computing the singular value decomposition (SVD) of a polynomial matrix. It takes the form of a sequential best rotation (SBR) algorithm and constitutes a generalisation of the Kogbetliantz technique for computing the SVD of conventional scalar matrices. It avoids “squaring” the matrix to be factorised, uses only unitary and paraunitary operations, and therefore exhibits a high degree of numerical stability.