dev | ml | math

## A First Attempt

Let $\mathbf{x}^\top=\begin{pmatrix}\mathbf{x}_0&\cdots&\mathbf{x}_n\end{pmatrix}$ for conformal $\mathbf{x}_i$ of sizes $s_0,s,\cdots,s$. Inspecting the $i$-th block of $\mathbf{x}=\mathbf{1}+ P\mathbf{x}$ yields for $1<i<n$ that $$\mathbf{x}_{i}=\mathbf{1}_s+U\mathbf{x}_{i-1}+C\mathbf{x}_{i}+V^\top \mathbf{x}_{i+1}\,\,,$$ which can be rewritten as $$\mathbf{x}_{i}=(I-C)^{-1}(\mathbf{1}_s+U\mathbf{x}_{i-1}+V^\top \mathbf{x}_{i+1})\,\,,$$ since $\rho(C)<1$ is implied by $\rho(P)<1$, as $C$ is the restriction of $P$ to the subspace for the $i$-th block, so $I-C$ is also nonsingular.

By itself, expressing the $i$-th block in terms of the $(i-1)$-st and $(i+1)$-st isn’t too helpful: such a recurrence doesn’t bottom out (at least not obviously). Naively, if $U$ was somehow nonsingular then we could express $\mathbf{x}{i-1}$ in terms of $\mathbf{x}_i,\mathbf{x}{i+1}$, which does clearly terminate.

## A Second Attempt

Writing $U=L\Sigma R^\top$ as the full SVD for unitary $s\times s$ matrices $L, R$, and $\Sigma=\begin{pmatrix} I_r & 0 0 & 0{s-r} \end{pmatrix}$ for the rank $r$ of $U$, let’s multiply each block of the equation $(I-P)\mathbf{x}=\mathbf{1}$ by $L^\top$ on the left and $R$ on the right. Note we don’t need to treat the edge case $r=0$ differently. Letting $\mathbf{y}_i= R^\top \mathbf{x}_i$ for every block $n\ge i >1$ we are left with for every $i$, $$\begin{gather*} &-L^\top L\Sigma R^\top R\mathbf{y}_{i-1}+ L^\top(I-C)R\mathbf{y}_i- L^\top V^\top R\mathbf{y}_{i+1}\\ =&\begin{pmatrix} -I_r & 0\\ 0 & 0 \end{pmatrix}\mathbf{y}_{i-1} + A\mathbf{y}_i+B\mathbf{y}_{i+1}\\ =&L^\top\mathbf{1}\,\,. \end{gather*}$$ Above, $A,B$ are suitably defined, where we note that since $L,R$ are unitary, $A$ is nonsingular, and we let $\mathbf{y}{n+1}=\mathbf{0}$ for the last equation.

Now, to consider fully determined systems, we need to think about re-indexing. Our current block indexing is by $s_0,s,\cdots,s$: $$\mathbf{y}^\top=\begin{pmatrix}\mathbf{x}_0&\mathbf{y}_1&\cdots&\mathbf{y}_{n-1}&\mathbf{x}_n & \mathbf{0}_s\end{pmatrix}\,\,,$$ but consider instead blocking by $s_0 + r, s, \cdots s$ by extending with more zeros so that $$(\mathbf{y}')^\top=\begin{pmatrix}\mathbf{y}_0'&\mathbf{y}_1'&\cdots&\mathbf{y}_{n-1}'&\mathbf{y}_n' & \mathbf{0}_s\end{pmatrix}\,\,,$$ where $(\mathbf{y}0’)^\top =\mathbf{x}_0^\top|(\mathbf{y}_1){1:r}^\top$, $(\mathbf{y}{n-1}’)^\top =(\mathbf{y}{n-1}){r+1:s}^\top|(\mathbf{x}_n){1:r}^\top$, $(\mathbf{y}{n}’)^\top =(\mathbf{x}{n}){r+1:s}^\top|\mathbf{0}_r^\top$. All this hard work yields the shifted equations $$\begin{pmatrix} A_{s-r,r} & A_{s-r}\\ -I_r & 0 \end{pmatrix}\mathbf{y}_{i-1}' + \begin{pmatrix} B_{s-r,r} & B_{s-r}\\ A_{r} & A_{r,s-r} \end{pmatrix}\mathbf{y}_i'+\begin{pmatrix} 0 & 0\\ B_{r} & A_{r,s-r} \end{pmatrix}\mathbf{y}_{i+1}' =\begin{pmatrix} (L^\top)_{s-r,s}\\(L^\top)_{r, s} \end{pmatrix}\mathbf{1}\,\,,$$ where we conformably break the $s\times s$ matrix $A=\begin{pmatrix} A_r & A{r,s-r} A_{s-r,r} & A_{s-r} \end{pmatrix}$, and similarly for others, e.g. $L^\top=\begin{pmatrix} (L^\top){r, s} (L^\top) {s-r,s} \end{pmatrix}$.

For all the marbles, we ask whether $\begin{pmatrix} A_{s-r,r} & A_{s-r} -I_r & 0 \end{pmatrix}$ is invertible. I haven’t figured this out yet (TODO). But, assuming it is, then we can solve the linear system above for $\mathbf{y}{i-1}’=T(\mathbf{y}_i,\mathbf{y}{i+1})$.

TODO make it square

$(\mathbf{y}_0,\mathbf{x}_1,\mathbf{x}_2)=T_0(\mathbf{x}_2,\mathbf{x}_3, \mathbf{x}_4,\mathbf{1}_s)=T_0T^{n-1}(\mathbf{x}_{n-1},\mathbf{x}_n,\mathbf{1}_s)\,\,,$

TODO exponentiate as above

Backsolve x

## A More Generic Solution

While the message-passing algorithm from the previous post works, it turns out it’s also possible to use a much more generic technique for solving block-Toeplitz (not necessarily tridiagonal) systems: circulant completion.

A block circulant matrix $\mathrm{BC}(X)$ is defined by a three-dimensional array $X$ of shape $m\times s\times s$, yielding a matrix of size $ms\times ms$ which is both block-Toeplitz and the $i$-th and $(m+i)$-th blocks are identical (modulo $m$).

Following De Mazancourt, block circulant matrices have an explicit inverse. Namely, if $A=\mathrm{BC}(X)$ then $A^{-1}=\mathrm{BC}(\mathcal{F}^{-1}(\mathcal{F} X)^{-1})$, where $\mathcal{F}$ is the DFT along the first axis (i.e., we apply $s^2$ independent DFTs entrywise along $X$) but the inverse operation is applied to each matrix defined by the last two axes. This holds assuming $A$ is invertible.

For a block-Toeplitz $B$, $A=\begin{pmatrix} B & F E & B \end{pmatrix}$ is an embedding into a block-circulant matrix of twice $B$’s size, with $E$, $F$ chosen with null diagonals and Toeplitz such that the resulting matrix is indeed Toeplitz. Then we simply pad the original system with zeros and enjoy a simple solution to block-Toeplitz systems!

import numpy as np

# columns[i] defines the (i+1)-th block diagonal

def block_toeplitz(rows, center, cols): assert len(rows) == len(cols) res = [] for i in range(len(rows) + 1): res.append(list(reversed(rows[:i])) + [center] + cols[:len(cols)-i]) return np.block(res)

np.random.seed(1234) r = [np.random.randint(10, size=(2, 2)), np.zeros((2, 2)), np.zeros((2, 2))] c = [np.zeros((2, 2)), np.random.randint(10, size=(2, 2)), np.zeros((2, 2))]

%matplotlib inline from matplotlib import pyplot as plt

T = block_toeplitz( r, 10 * np.eye(2), c) plt.imshow(T) plt.axis(‘off’) plt.show()

def block_circulant(c): res = [] for i in range(len(c)): res.append(c[len(c) - i:] + c[:len(c)-i]) return np.block(res)

plt.imshow(block_circulant([np.eye(2), 2 * np.eye(2), 3 * np.eye(2)[[1, 0]]]), resample=False) plt.axis(‘off’) plt.show()

def t2c(rows, center, cols): return [center] + cols + [np.zeros_like(center)] + list(reversed(rows))

C = block_circulant(t2c( r, 10 * np.eye(2), c)) plt.imshow(C, resample=False) plt.axis(‘off’) plt.show()

T = block_toeplitz( r, 10 * np.eye(2), c) C = block_circulant(t2c( r, 10 * np.eye(2), c))

x = np.random.rand(T.shape[0]) s1 = np.linalg.solve(T, x) s2 = np.linalg.solve(C, np.concatenate([x, np.zeros_like(x)])) np.linalg.norm(s1 - s2[:len(x)])