Schur problem (or Carathéodory-Fejér interpolation)
Given complex numbers , find a function
which is analytic and bounded by 1 on the unit disk. The method of solving this problem as well as similar problems (e.g. solving Toeplitz systems and Nevanlinna-Pick interpolation) is known as the Schur algorithm (also called coefficient stripping or layer stripping). One of the algorithm's most important properties is that it generates n + 1orthogonal polynomials which can be used as orthonormal basis functions to expand any nth-order polynomial. It is closely related to the Levinson algorithm though Schur algorithm is numerically more stable and better suited to parallel processing.
Schur function
Consider the Carathéodory function of a unique probability measure on the unit circle given by
where implies . Then the association
sets up a one-to-one correspondence between Carathéodory functions and Schur functions given by the inverse formula:
Schur algorithm
Schur's algorithm is an iterative construction based on Möbius transformations that maps one Schur function to another. The algorithm defines an infinite sequence of Schur functions and Schur parameters (also called Verblunsky coefficient or reflection coefficient) via the recursion:
which stops if . One can invert the transformation as
or, equivalently, as continued fraction expansion of the Schur function
by repeatedly using the fact that
↑Schur, J. (1918), "Über die Potenzreihen, die im Innern des Einheitkreises beschränkten sind. I, II", Journal für die reine und angewandte Mathematik, Operator Theory: Advances and Applications, vol.147, pp.205–232, I. Schur Methods in Operator Theory and Signal Processing in: Operator Theory: Advances and Applications, vol. 18, Birkhäuser, Basel, 1986 (English translation), doi:10.1007/978-3-0348-5483-2, ISBN978-3-0348-5484-9{{citation}}: ISBN / Date incompatibility (help)
↑Chung, Jin-Gyun; Parhi, Keshab K. (1996). Pipelined Lattice and Wave Digital Recursive Filters. The Kluwer International Series in Engineering and Computer Science. Boston, MA: Springer US. p.79. doi:10.1007/978-1-4613-1307-6. ISBN978-1-4612-8560-1. ISSN0893-3405.
↑Hayes, Monson H. (1996). Statistical digital signal processing and modeling. John Wiley & Son. p.242. ISBN978-0-471-59431-4. OCLC34243409.