# Our Paper on Realizing a Graph on Random Points

Our paper, titled How to Realize a Graph on Random Points, has been accepted at CCCG (Canadian Conference on Computational Geometry) 2019. I’ll write more about it in future posts.

Abstract:

# Notes on the PCP Theorem and the Hardness of Approximation: Part 1

In this note, we are going to state the PCP theorem and its relation to the hardness of approximating some NP-hard problem.

## PCP Theorem: the Interactive Proof View

Intuitively, a PCP (Probabilistically Checkable Proof) system is an interactive proof system where the verifier is given $r(n)$ random bits and he is allowed to look into the proof $\pi$ in $q(n)$ many locations. If the string $x \in \{0,1\}^n$ is indeed in the language, then there exists a proof so that the verifier always accepts. However, if $x$ is not in the language, no prover can convince this verier with probability more than $1/2$. The proof has to be short i.e., of size at most $q(n) 2^{r(n)}$. This class of language is designated as PCP[r(n), q(n)].

Theorem A (PCP theorem). Every NP language has a highly efficient PCP verifier. In particular, $NP = PCP[O(\log n), O(1)]$.

# 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 $x,y$.

# Eigenvalues of the Laplacian Matrix of the Complete Graph

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 $n-1$.

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

In this post, we provide a proof of Kirchhoff’s Matrix Tree theorem  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 $L\ \}$.

# 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:

1. The reader is a beginner, like me, and have already glanced through the Spielman-Srivastava paper (from now on, the SS paper).
2. 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.
3. 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).