# 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 L$_G$, where n is the number of vertices in the graph, with the entries:
    $l_{kl}:={\begin{cases}\sum _{v_{k}\rightarrow v_{l}}w_{kl}{\mbox{ if}}\ k=l,\\-w_{kl}{\mbox{ if}}\ v_{k}\rightarrow v_{l},\\0,{\mbox{ otherwise,}}\end{cases}}$
where v$_k$ → v$_l$ means that there is a directed edge from vertex v$_k$ to the vertex v$_l$, 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 L$_G$ 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.
    $L_{G}={\begin{pmatrix}A&B\\C&D\end{pmatrix}}$
The Schur complement of the matrix $L_{G}$ w/respect of the invertible block D is the matrix $L_{G}/D=A-BD^{-1}C.$
    Exercise (*). Prove the following determinant identity:$\det L_{G}=\det(L_{G}/D)\det D.$
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 v$_k$ occupies, is the boundary vertex v$_l$. For a finite connected graph the columns of the matrix W(G) add up to 1.
    Exercise (**). Derive an explicit formula for the matrix $W_{G}$ in terms of the blocks of Laplace matrix $L_{G}$of the graph G: $W_{G}=I-D_{A}^{-1}(A-BD^{-1}C).$
    Exercise (***). Prove the following expansion formulas for entries and blocks of the matrix W(G),
    $w_{kl}=\sum _{v_{k}{\xrightarrow[]{path}}v_{l}}\prod _{e=(p,q)\in path}w(e)/l_{pp},$
Hint: use the Leibniz definition of the determinant
    $w_{kl}\det D={\frac {1}{l_{kk}}}\sum _{v_{k}{\xrightarrow {path}}v_{l}}\prod _{e\in path}l(e)\det D({\tilde {path}},{\tilde {path}}),$
where ${\tilde {path}}=V-(\partial G\cup path).$
    $\det W(P,Q)\det D=\pm {\frac {\sum _{\sigma \in S_{n}}(-1)^{\operatorname {sgn}(\sigma )}\sum _{p_{k}{\xrightarrow {paths}}q_{\sigma _{k}}}\prod _{e\in paths}l(e)\det D({\tilde {paths}},{\tilde {paths}})}{\prod _{p\in P}l_{pp}}},$
where
    ${\tilde {paths}}=V-(\partial G\cup paths).$
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
    $(-1)^{\frac {n(n+1)}{2}}\det \Lambda _{G}(P,Q)\geq 0,$
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],
    ${\begin{pmatrix}1&1&1&1&\ldots \\1&2&3&4&\ldots \\1&3&6&\ldots &\ddots \\1&4&10&\ldots &\ddots \\1&\ldots &\ldots &\ddots &\ddots \\\end{pmatrix}}={\begin{pmatrix}1&0&0&0&\ldots \\1&1&0&0&\ldots \\1&2&1&0&\ddots \\1&3&3&\ldots &\ddots \\1&\ldots &\ldots &\ddots &\ddots \\\end{pmatrix}}{\begin{pmatrix}1&1&1&1&\ldots \\0&1&2&3&\ldots \\0&0&1&3&\ddots \\0&0&0&\ldots &\ddots \\0&\ldots &\ldots &\ddots &\ddots \\\end{pmatrix}}.$