Schur class

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In complex analysis, the Schur class is the set of holomorphic functions f(z) defined on the open unit disk 𝔻={z:|z|<1} and satisfying |f(z)|1 that solve the Schur problem: Given complex numbers c0,c1,,cn, find a function

f(z)=j=0ncjzj+j=n+1nfjzj

which is analytic and bounded by 1 on the unit disk.[1] 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 + 1 orthogonal polynomials which can be used as orthonormal basis functions to expand any nth-order polynomial.[2] It is closely related to the Levinson algorithm though Schur algorithm is numerically more stable and better suited to parallel processing.[3]

Schur function

Consider the CarathĂ©odory function of a unique probability measure dμ on the unit circle 𝕋={z:|z|=1} given by

F(z)=eiθ+zeiθzdμ(θ)

where dμ(θ)=1 implies F(0)=1.[4] Then the association

F(z)=1+zf(z)1zf(z)

sets up a one-to-one correspondence between Carathéodory functions and Schur functions f(z) given by the inverse formula:

f(z)=z1(F(z)1F(z)+1)

Schur algorithm

Schur's algorithm is an iterative construction based on Möbius transformations that maps one Schur function to another.[4][5] The algorithm defines an infinite sequence of Schur functions ff0,f1,,fn, and Schur parameters γ0,γ1,,γn, (also called Verblunsky coefficient or reflection coefficient) via the recursion:[6]

fj+1=1zfj(z)γj1γjfj(z),fj(0)γj𝔻,

which stops if fj(z)eiθ=γj𝕋. One can invert the transformation as

f(z)f0(z)=γ0+zf1(z)1+γ0zf1(z)

or, equivalently, as continued fraction expansion of the Schur function

f0(z)=γ0+1|γ0|2γ0+1zγ1+z(1|γ1|2)γ1+1zγ2+

by repeatedly using the fact that

fj(z)=γj+1|γj|2γj+1zfj+1(z).

See also

References

  1. ↑ Schur, J. (1918), "Über die Potenzreihen, die im Innern des Einheitkreises beschrĂ€nkten sind. I, II", Journal fĂŒr die reine und angewandte Mathematik, Operator Theory: Advances and Applications 147: 205–232, I. Schur Methods in Operator Theory and Signal Processing in: Operator Theory: Advances and Applications, vol. 18, BirkhĂ€user, Basel, 1986 (English translation), doi:10.1007/978-3-0348-5483-2, ISBN 978-3-0348-5484-9 
  2. ↑ 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. ISBN 978-1-4612-8560-1. 
  3. ↑ Hayes, Monson H. (1996). Statistical digital signal processing and modeling. John Wiley & Son. p. 242. ISBN 978-0-471-59431-4. OCLC 34243409. https://www.worldcat.org/oclc/34243409. 
  4. ↑ 4.0 4.1 Simon, Barry (2005), Orthogonal polynomials on the unit circle. Part 1. Classical theory, American Mathematical Society Colloquium Publications, 54, Providence, R.I.: American Mathematical Society, ISBN 978-0-8218-3446-6, https://books.google.com/books?id=d94r7kOSnKcC 
  5. ↑ Conway, John B. (1978). Functions of One Complex Variable I (Graduate Texts in Mathematics 11). Springer-Verlag. p. 127. ISBN 978-0-387-90328-6. 
  6. ↑ Simon, Barry (2010), SzegƑ's theorem and its descendants: spectral theory for LÂČ perturbations of orthogonal polynomials, Princeton University Press, ISBN 978-0-691-14704-8, https://books.google.com/books?id=e9R5gcz_x0YC