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The Hermitian positive-definite Gohberg–Semencul formula is exact and quadratic

Statement

Let Tn=(ti−j)0≤i,j<n be Hermitian positive definite, and let b∈Cn. Run General-RHS Hermitian Toeplitz solve by reflection recursion and Gohberg–Semencul in exact arithmetic. It returns x=Tn−1b. If u=Tn−1e0 and v=Tn−1en−1 are the first and last inverse columns, then Tn−1=1u0(L(u)L(u)∗−L(Znv)L(Znv)∗),u0>0. The recursion and four triangular Toeplitz products use O(n2) complex arithmetic operations and O(n) storage beyond the input, output, and lag vector. This is an exact-arithmetic cost statement and makes no finite-precision stability claim.

Facts & Assumptions

Given: An integer n≥1, a Hermitian positive-definite Toeplitz matrix Tn, its lag sequence, and an arbitrary b∈Cn.

[F1]

Tij=ti−j, Hermitian symmetry is t−r=tr‾, and Jn reverses coordinates. Toeplitz, circulant, and finite-index conventions for structured solvers

[F2]

The conjugate-reversal recursion produces Tkϕk=gk and νk=t0−gk∗ϕk>0 for 0≤k<n. Complex Hermitian Toeplitz bordering yields reflection coefficients and positive pivots

[F3]

The algorithm forms v=(−Jn−1ϕn−1‾/νn−1,1/νn−1)T, u=Jnv‾, and applies the displayed triangular Toeplitz expression to b. General-RHS Hermitian Toeplitz solve by reflection recursion and Gohberg–Semencul

[F5]

Matrix products use the finite entrywise convention of Rectangular matrix multiplication and the identity matrix In, including zero-sized shapes. Here the adjoint is the conjugate transpose, (M∗)ij=Mji‾.

Proof

1.1givenF2F3F4algebra

If n=1, then T1=[t0] with t0>0, ϕ0 is empty, ν0=t0, and u=v=(1/t0). Also Z1v=0 and L(u)=[1/t0], so the algorithm returns b/t0=T1−1b. Hence assume n≥2.

2.1givenF1F2F4step 1.1algebra

Put g=(t1,…,tn−1)T, c=Jn−1g‾, and ϕ=ϕn−1. Then Tn=(Tn−1cc∗t0) and Tn−1ϕ=g by [F1]–[F2]. The reversal identity gives Tn−1−1c=Jn−1ϕ‾. Moreover, c∗Tn−1−1c=gTϕ‾=g∗ϕ‾=g∗ϕ, because Tn−1∗=Tn−1 implies (Tn−1−1)∗=(Tn−1∗)−1=Tn−1−1. Thus the Schur complement is t0−c∗Tn−1−1c=νn−1>0. In Tnv=en−1, the top block equation gives v0:n−2=−Tn−1−1c vn−1=−Jn−1ϕ‾ vn−1; substituting this in the last equation gives νn−1vn−1=1. Hence v=(−Jn−1ϕ‾/νn−11/νn−1). Thus v=Tn−1en−1.

3.1givenF1F2step 2.1algebra

Entrywise, JnTn‾Jn=Tn because tj−i‾=ti−j. Also X∗=(Tn−1)∗=(Tn∗)−1=Tn−1=X. Taking inverses in the reversal identity gives X=JnX‾Jn, so u=Jnv‾=Tn−1e0. Since vn−1=1/νn−1 is positive real, u0=vn−1=1/νn−1>0. The first-column block equation gu0+Tn−1u1:n−1=0 gives u1:n−1=−u0ϕ, and step 2.1 gives v0:n−2=−u0Jn−1ϕ‾.

4.1givenF1F4F5step 2.1step 3.1algebra

Set X=Tn−1 and D=X−ZnXZn∗. By step 3.1, X is Hermitian and TnX=In. Since the first row and column of Zn and Zn∗ vanish, D0j=X0j=uj‾ and Di0=Xi0=ui; these are the corresponding first row and column of (uu∗−(Znv)(Znv)∗)/u0. For 1≤i,j<n, subtract (TnX)i−1,j−1=Ii−1,j−1 from (TnX)ij=Iij. The identity entries agree because i=j exactly when i−1=j−1. Reindexing the second finite sum gives ∑k=1n−1ti−k(Xk,j−Xk−1,j−1)=−tiX0j+ti−nXn−1,j−1. The coefficient matrix is Tn−1, while X0j=uj‾ and Xn−1,j−1=vj−1‾.

5.1F2step 3.1step 4.1algebra

The right side in step 4.1 is −g uj‾+(Jn−1g‾) vj−1‾. Applying Tn−1−1 and using [F2] and the reversal identity gives Dij=−ϕi−1uj‾+(Jn−1ϕ‾)i−1vj−1‾. By step 3.1, ui=−u0ϕi−1 and vi−1=−u0(Jn−1ϕ‾)i−1. Therefore, for every i,j≥1, Dij=(uiuj‾−vi−1vj−1‾)/u0. Together with the boundary entries in step 4.1, this proves D=uu∗−(Znv)(Znv)∗u0.

6.1F3F5step 5.1algebra

The shift is nilpotent, Znn=0. Summing ZnqD(Zn∗)q for q=0,…,n−1 telescopes to X−ZnnX(Zn∗)n=X. The columns of L(u) are Znqu, and the columns of L(Znv) are Znq(Znv), so substituting the identity of step 5.1 into the telescoping sum yields the stated Gohberg–Semencul formula. The algorithm’s output is therefore Xb=Tn−1b.

7.1F2F3step 6.1algebra∎

The reflection recursion costs O(n2) operations and O(n) workspace by [F2]. Each triangular Toeplitz product in step 6.1 evaluates at most n(n+1)/2 products and a comparable number of additions; the four products therefore cost O(n2). The two generator vectors and intermediate vectors require O(n) storage, and the algorithm never materializes an n×n triangular factor.

Remarks

The proof identifies an exact inverse representation. It does not establish that finite-precision Levinson or Gohberg–Semencul evaluation is backward stable; pivot and cancellation growth require separate analysis.

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