In a Hermitian Matrix, the Eigenvectors of Different Eigenvalues are Orthogonal

This is an elementary (yet important) fact in matrix analysis.

Statement

Let $M$ be an $n\times n$ complex Hermitian matrix which means $M=M^*$ where $*$ denotes the conjugate transpose operation. Let $\lambda_1, \neq \lambda_2$ be two different eigenvalues of $M$ . Let $x, y$ be the two eigenvectors of $M$ corresponding to the two eigenvalues $\lambda_1$ and $\lambda_2$ , respectively.

Then the following is true:

$\boxed{\lambda_1 \neq \lambda_2 \Longrightarrow \langle x, y \rangle = 0.}$

Here $\langle x,y \rangle := y^*x$ denotes the usual inner product of two vectors $x,y$ .

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Eigenvalues of the Laplacian Matrix of the Complete Graph

A complete graph (source: David Benbennick)

We will show that the eigenvalues of the $n\times n$ Laplacian matrix $L$ of the complete graph $K_n$ are $\{0,n\}$ where the eigenvalue $0$ has (algebraic) multiplicity $1$ and the eigenvalue $n$ has multiplicity $n-1$ .

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Kirchhoff’s Matrix Tree Theorem for Counting Spanning Trees — A Proof for Beginners

Rectilinear minimum spanning tree (source: Rocchini)

In this post, we provide a proof of Kirchhoff’s Matrix Tree theorem [1] which is quite beautiful in our biased opinion. This is a 160-year-old theorem which connects several fundamental concepts of matrix analysis and graph theory (e.g., eigenvalues, determinants, cofactor/minor, Laplacian matrix, incidence matrix, connectedness, graphs, and trees etc.). There are already many expository articles/proof sketches/proofs of this theorem (for example, see [4-6, 8,9]). Here we compile yet-another-expository-article and present arguments in a way which should be accessible to the beginners.

Statement

Suppose $L$ is the Laplacian of a connected simple graph $G$ with $n$ vertices. Then the number of spanning trees in $G$ , denoted by $t(G)$ , is the following:

$t(G)=\frac{1}{n} \times \{$ the product of all non-zero eigenvalues of $L\ \}$ .

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Spectral Sparsification by Spielman and Srivastava: How They Connected the Dots

In this post, I will discuss my own understanding of the spectral sparsification paper by Daniel Spielman and Nikhil Srivastava (STOC 2008). I will assume the following:

The reader is a beginner, like me, and have already glanced through the Spielman-Srivastava paper (from now on, the SS paper).
The reader has, like me, a basic understanding of spectral sparsification and associated concepts of matrix analysis. I will assume that she has read and understood the Section 2 (Preliminaries) of the SS paper.
The reader holds a copy of the SS paper while reading my post.

First, I will mention the main theorems (actually, I will mention only what they “roughly” say).

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