Schur class

In complex analysis, the Schur class is the set of holomorphic functions f ( z ) {\displaystyle f(z)} defined on the open unit disk D = { z C : | z | < 1 } {\displaystyle \mathbb {D} =\{z\in \mathbb {C} :|z|<1\}} and satisfying | f ( z ) | 1 {\displaystyle |f(z)|\leq 1} that solve the Schur problem: Given complex numbers c 0 , c 1 , , c n {\displaystyle c_{0},c_{1},\dotsc ,c_{n}} , find a function

f ( z ) = j = 0 n c j z j + j = n + 1 n f j z j {\displaystyle f(z)=\sum _{j=0}^{n}c_{j}z^{j}+\sum _{j=n+1}^{n}f_{j}z^{j}}

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 Carathéodory function of a unique probability measure d μ {\displaystyle d\mu } on the unit circle T = { z C : | z | = 1 } {\displaystyle \mathbb {T} =\{z\in \mathbb {C} :|z|=1\}} given by

F ( z ) = e i θ + z e i θ z d μ ( θ ) {\displaystyle F(z)=\int {\frac {e^{i\theta }+z}{e^{i\theta }-z}}d\mu (\theta )}

where d μ ( θ ) = 1 {\displaystyle \int d\mu (\theta )=1} implies F ( 0 ) = 1 {\displaystyle F(0)=1} .[4] Then the association

F ( z ) = 1 + z f ( z ) 1 z f ( z ) {\displaystyle F(z)={\frac {1+zf(z)}{1-zf(z)}}}

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

f ( z ) = z 1 ( F ( z ) 1 F ( z ) + 1 ) {\displaystyle f(z)=z^{-1}\left({\frac {F(z)-1}{F(z)+1}}\right)}

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 f f 0 , f 1 , , f n , {\displaystyle f\equiv f_{0},f_{1},\dotsc ,f_{n},\dotsc } and Schur parameters γ 0 , γ 1 , , γ n , {\displaystyle \gamma _{0},\gamma _{1},\dotsc ,\gamma _{n},\dotsc } (also called Verblunsky coefficient or reflection coefficient) via the recursion:[6]

f j + 1 = 1 z f j ( z ) γ j 1 γ j ¯ f j ( z ) , f j ( 0 ) γ j D , {\displaystyle f_{j+1}={\frac {1}{z}}{\frac {f_{j}(z)-\gamma _{j}}{1-{\overline {\gamma _{j}}}f_{j}(z)}},\quad f_{j}(0)\equiv \gamma _{j}\in \mathbb {D} ,}

which stops if f j ( z ) e i θ = γ j T {\displaystyle f_{j}(z)\equiv e^{i\theta }=\gamma _{j}\in \mathbb {T} } . One can invert the transformation as

f ( z ) f 0 ( z ) = γ 0 + z f 1 ( z ) 1 + γ 0 ¯ z f 1 ( z ) {\displaystyle f(z)\equiv f_{0}(z)={\frac {\gamma _{0}+zf_{1}(z)}{1+{\overline {\gamma _{0}}}zf_{1}(z)}}}

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

f 0 ( z ) = γ 0 + 1 | γ 0 | 2 γ 0 ¯ + 1 z γ 1 + z ( 1 | γ 1 | 2 ) γ 1 ¯ + 1 z γ 2 + {\displaystyle f_{0}(z)=\gamma _{0}+{\frac {1-|\gamma _{0}|^{2}}{{\overline {\gamma _{0}}}+{\frac {1}{z\gamma _{1}+{\frac {z(1-|\gamma _{1}|^{2})}{{\overline {\gamma _{1}}}+{\frac {1}{z\gamma _{2}+\cdots }}}}}}}}}

by repeatedly using the fact that

f j ( z ) = γ j + 1 | γ j | 2 γ j ¯ + 1 z f j + 1 ( z ) . {\displaystyle f_{j}(z)=\gamma _{j}+{\frac {1-|\gamma _{j}|^{2}}{{\overline {\gamma _{j}}}+{\frac {1}{zf_{j+1}(z)}}}}.}

See also

References

  1. ^ 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, 147: 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, 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. ISSN 0893-3405.
  3. ^ Hayes, Monson H. (1996). Statistical digital signal processing and modeling. John Wiley & Son. p. 242. ISBN 978-0-471-59431-4. OCLC 34243409.
  4. ^ a b Simon, Barry (2005), Orthogonal polynomials on the unit circle. Part 1. Classical theory, American Mathematical Society Colloquium Publications, vol. 54, Providence, R.I.: American Mathematical Society, ISBN 978-0-8218-3446-6, MR 2105088
  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), Szegő's theorem and its descendants: spectral theory for L² perturbations of orthogonal polynomials, Princeton University Press, ISBN 978-0-691-14704-8