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Complex Hermitian Toeplitz bordering yields reflection coefficients and positive pivots
Statement
Let and let be Hermitian positive definite, so . For , set , with empty, and for set , with empty. Define empty and . For each , let reverse coordinates, put , and set Every denominator used is strictly positive; and for . Computing through order takes complex arithmetic operations and working storage. This is the complex matrix-bordering extension; the real ACVF forecast procedure remains Durbin–Levinson forecast recursion.
Facts & Assumptions
Given: A finite Hermitian positive-definite Toeplitz matrix , its lags, and the displayed recursively defined vectors and scalars.
The zero-based Toeplitz convention is , and Hermitian symmetry is . Toeplitz, circulant, and finite-index conventions for structured solvers
A Hermitian positive-definite square matrix admits a Cholesky factorization with positive diagonal, and hence is invertible. A matrix admits a Cholesky factorisation with positive diagonal exactly when it is Hermitian positive definite, and that factor is unique
Matrix products are defined by finite sums over the shared index; . Rectangular matrix multiplication and the identity matrix , including zero-sized shapes Toeplitz, circulant, and finite-index conventions for structured solvers
Proof
Each leading block is positive definite: if in , append zeros to obtain in ; then by [F1]. Thus every nonempty is invertible by [F2], and by testing the first coordinate vector. The empty case has the stated empty solution.
Reversal is an involutive real permutation, so . Reversal and Hermitian symmetry give : its entry is . Taking inverses also gives , and the same identity implies for every .
At order , both and are the empty identity and by step 1.1. This is the induction base.
For , the formula gives and , so directly.
Fix and assume , . By step 2.1 and , . Since is Hermitian and invertible, , so is real. Therefore . Thus the Schur pivot of after is . It is positive by evaluating the positive quadratic form of at .
Fix . The first rows of follow from and (step 2.1). The last row is by the definition of and the pivot identity in step 3.1. Thus .
For every update , expansion gives , since is real; hence . By step 2.3 when and step 4.1 when , . Set ; step 2.1 gives , and Hermitian symmetry gives . Thus is the Schur complement of in . This leading block is HPD by step 1.1; evaluating its quadratic form at proves , including the terminal case .
At stage , one inner product and one vector update use operations; summing these costs over gives . The input lags and current and next coefficient vectors require storage, and no earlier coefficient vector is needed after its update.
Depends on
- Durbin–Levinson forecast recursion
- Rectangular matrix multiplication and the identity matrix $I_n$, including zero-sized shapes
- Toeplitz, circulant, and finite-index conventions for structured solvers
- A matrix admits a Cholesky factorisation with positive diagonal exactly when it is Hermitian positive definite, and that factor is unique
Used by
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Sources
- MIT OpenCourseWare 6.341, Discrete-Time Signal Processing, Lecture 13: The Levinson-Durbin Recursion (standard reference, not scraped)
- Ralf Hiptmair, Numerical Methods for Computational Science and Engineering, Lecture Document, §4.5.3 (standard reference, not scraped)