Title :
On the complexity of IQML algorithms
Author :
Clark, Michael P. ; Scharf, Louis L.
Author_Institution :
Colorado Univ., Boulder, CO, USA
fDate :
7/1/1992 12:00:00 AM
Abstract :
The authors study the computational complexity of two methods for solving least squares and maximum likelihood modal analysis problems. In particular, they consider the Steiglitz-McBride and iterative quadratic maximum likelihood (IQML) algorithms. J.H. McClellan and D. Lee (ibid., vol.39, no.2, p.509-12, 1991) have shown the iterations of the two methods to be equivalent. However, they suggest that the Steiglitz-McBride algorithm may be computationally preferable. A method for reducing the dimension of the matrix inversion required at each iteration of IQML is provided. The resulting reduction in the computation makes the computational complexity of IQML commensurate with that of the Steiglitz-McBride algorithm
Keywords :
computational complexity; iterative methods; least squares approximations; signal processing; IQML algorithms; Steiglitz-McBride algorithm; computational complexity; iterative quadratic maximum likelihood; least-squares modal analysis; matrix inversion; maximum likelihood modal analysis; signal processing; Computational complexity; Design methodology; Filters; Iterative algorithms; Iterative methods; Least squares methods; Maximum likelihood estimation; Modal analysis; Multidimensional systems; Polynomials;
Journal_Title :
Signal Processing, IEEE Transactions on
