Root-exchange property of constrained linear predictive models

In recent works, we have studied linear predictive models constrained by time-domain filters. In the present study, studied the one-dimensional case in more detail. Firstly, we obtain root-exchange properties between the roots of an all-pole model and corresponding constraints. Secondly, using the root-exchange property we can construct a novel matrix decomposition A/sup T/RA/sup #/ = I, where R is a real positive definite symmetric Toeplitz matrix, superscript /sup #/ signifies reversal of rows and I is the identity matrix. In addition, there exists also an inverse matrix decomposition C/sup T/R/sup -1/C/sup #/ = I, where C /spl isin/ C is a Vandermonde matrix. Potential applications are discussed.