# On 2D Inverse Problems/Special matrices

An important object containing information about a weighted graph G(V,E,w) is its Laplacian matrix. Given a numbering of the vertices of the graph, it's an n by n square matrix LG, where n is the number of vertices in the graph, with the entries:

${\displaystyle l_{kl}:={\begin{cases}\sum _{v_{k}\rightarrow v_{l}}w_{kl}{\mbox{ if}}\ k=l,\\-w_{kl}{\mbox{ if}}\ v_{k}\rightarrow v_{l},\\0,{\mbox{ otherwise,}}\end{cases}}}$

where vkvl means that there is a directed edge from vertex vk to the vertex vl, and where w is the weight function.

Exercise (*). Given a directed graph G without cycles, prove that one can number its vertices so that the corresponding Laplacian matrix LG is triangular.

Given a weighted graph with boundary it is often convenient to number its boundary vertices first and to write its Laplacian matrix in the block form.

${\displaystyle L_{G}={\begin{pmatrix}A&B\\C&D\end{pmatrix}}}$

The Schur complement of the matrix ${\displaystyle L_{G}}$ w/respect of the invertible block D is the matrix ${\displaystyle L_{G}/D=A-BD^{-1}C.}$

Exercise (*). Prove the following determinant identity:${\displaystyle \det L_{G}=\det(L_{G}/D)\det D.}$

The following matrix W(G) consisting of random walk exiting probabilities (sums over weighted paths in a graph) plays an important role as boundary measurement for inverse problems. Suppose a weighted graph G has N boundary nodes, then the kl 'th entry of the N by N matrix equals to the probability that the first boundary vertex, that a particle starting its random walk at the boundary vertex vk occupies, is the boundary vertex vl. For a finite connected graph the columns of the matrix W(G) add up to 1.

Exercise (**). Derive an explicit formula for the matrix ${\displaystyle W_{G}}$ in terms of the blocks of Laplace matrix ${\displaystyle L_{G}}$of the graph G: ${\displaystyle W_{G}=I-D_{A}^{-1}(A-BD^{-1}C).}$
Exercise (***). Prove the following expansion formulas for entries and blocks of the matrix W(G),
• for two boundary vertices pk and pl of a graph G
${\displaystyle w_{kl}=\sum _{v_{k}{\xrightarrow[{}]{path}}v_{l}}\prod _{e=(p,q)\in path}w(e)/l_{pp},}$

Hint: use the Leibniz definition of the determinant

• for two distinct boundary vertices vk and vl of a graph G
${\displaystyle w_{kl}\det D={\frac {1}{l_{kk}}}\sum _{v_{k}{\xrightarrow {path}}v_{l}}\prod _{e\in path}l(e)\det D({\tilde {path}},{\tilde {path}}),}$

where ${\displaystyle {\tilde {path}}=V-(\partial G\cup path).}$

• for two disjoint subsets of boundary vertices P and Q of size n of a graph G, see [6],[7] and [14]
${\displaystyle \det W(P,Q)\det D=\pm {\frac {\sum _{\sigma \in S_{n}}(-1)^{\operatorname {sgn}(\sigma )}\sum _{p_{k}{\xrightarrow {paths}}q_{\sigma _{k}}}\prod _{e\in paths}l(e)\det D({\tilde {paths}},{\tilde {paths}})}{\prod _{p\in P}l_{pp}}},}$

where

${\displaystyle {\tilde {paths}}=V-(\partial G\cup paths).}$

The exercises above provide a bridge b/w connectivity property of graph G and ranks of submatrices of its Laplacian matrix L(G) and the matrix of hitting probabilities W(G).

Exercise (*). Let G be a planar graph w/natural boundary, numbered circulary. Let P and Q be two non-interlacing subsets of boundary nodes of size n. Prove that
${\displaystyle (-1)^{\frac {n(n+1)}{2}}\det \Lambda _{G}(P,Q)\geq 0,}$

w/the strict inequality iff there is a disjoint set of paths from P to Q.

Exercise (*). Show that the numbers of paths in the following graph are equal to the binomial coefficients.

Gluing the graphs w/out loops corresponds to multiplication of the weighted paths matrices.

Exercise (**). Use the result of the previous exercise to it to prove the following Pascal triangle identity, see[13],
${\displaystyle {\begin{pmatrix}1&1&1&1&\ldots \\1&2&3&4&\ldots \\1&3&6&\ldots &\ddots \\1&4&10&\ldots &\ddots \\1&\ldots &\ldots &\ddots &\ddots \\\end{pmatrix}}={\begin{pmatrix}1&0&0&0&\ldots \\1&1&0&0&\ldots \\1&2&1&0&\ddots \\1&3&3&\ldots &\ddots \\1&\ldots &\ldots &\ddots &\ddots \\\end{pmatrix}}{\begin{pmatrix}1&1&1&1&\ldots \\0&1&2&3&\ldots \\0&0&1&3&\ddots \\0&0&0&\ldots &\ddots \\0&\ldots &\ldots &\ddots &\ddots \\\end{pmatrix}}.}$