# Network & Matrix Computations

### CIVL 2123

$\newcommand{\mat}[1]{\boldsymbol{#1}} \renewcommand{\vec}[1]{\boldsymbol{\mathrm{#1}}} \newcommand{\vecalt}[1]{\boldsymbol{#1}} \newcommand{\conj}[1]{\overline{#1}} \newcommand{\normof}[1]{\|#1\|} \newcommand{\onormof}[2]{\|#1\|_{#2}} \newcommand{\itr}[2]{#1^{(#2)}} \newcommand{\itn}[1]{^{(#1)}} \newcommand{\eps}{\varepsilon} \newcommand{\kron}{\otimes} \DeclareMathOperator{\diag}{diag} \DeclareMathOperator{\trace}{trace} \newcommand{\prob}{\mathbb{P}} \newcommand{\probof}[1]{\prob\left\{ #1 \right\}} \newcommand{\pmat}[1]{\begin{pmatrix} #1 \end{pmatrix}} \newcommand{\bmat}[1]{\begin{bmatrix} #1 \end{bmatrix}} \newcommand{\spmat}[1]{\left(\begin{smallmatrix} #1 \end{smallmatrix}\right)} \newcommand{\sbmat}[1]{\left[\begin{smallmatrix} #1 \end{smallmatrix}\right]} \newcommand{\RR}{\mathbb{R}} \newcommand{\CC}{\mathbb{C}} \newcommand{\eye}{\mat{I}} \newcommand{\mA}{\mat{A}} \newcommand{\mB}{\mat{B}} \newcommand{\mC}{\mat{C}} \newcommand{\mD}{\mat{D}} \newcommand{\mE}{\mat{E}} \newcommand{\mF}{\mat{F}} \newcommand{\mG}{\mat{G}} \newcommand{\mH}{\mat{H}} \newcommand{\mI}{\mat{I}} \newcommand{\mJ}{\mat{J}} \newcommand{\mK}{\mat{K}} \newcommand{\mL}{\mat{L}} \newcommand{\mM}{\mat{M}} \newcommand{\mN}{\mat{N}} \newcommand{\mO}{\mat{O}} \newcommand{\mP}{\mat{P}} \newcommand{\mQ}{\mat{Q}} \newcommand{\mR}{\mat{R}} \newcommand{\mS}{\mat{S}} \newcommand{\mT}{\mat{T}} \newcommand{\mU}{\mat{U}} \newcommand{\mV}{\mat{V}} \newcommand{\mW}{\mat{W}} \newcommand{\mX}{\mat{X}} \newcommand{\mY}{\mat{Y}} \newcommand{\mZ}{\mat{Z}} \newcommand{\mLambda}{\mat{\Lambda}} \newcommand{\mPbar}{\bar{\mP}} \newcommand{\ones}{\vec{e}} \newcommand{\va}{\vec{a}} \newcommand{\vb}{\vec{b}} \newcommand{\vc}{\vec{c}} \newcommand{\vd}{\vec{d}} \newcommand{\ve}{\vec{e}} \newcommand{\vf}{\vec{f}} \newcommand{\vg}{\vec{g}} \newcommand{\vh}{\vec{h}} \newcommand{\vi}{\vec{i}} \newcommand{\vj}{\vec{j}} \newcommand{\vk}{\vec{k}} \newcommand{\vl}{\vec{l}} \newcommand{\vm}{\vec{l}} \newcommand{\vn}{\vec{n}} \newcommand{\vo}{\vec{o}} \newcommand{\vp}{\vec{p}} \newcommand{\vq}{\vec{q}} \newcommand{\vr}{\vec{r}} \newcommand{\vs}{\vec{s}} \newcommand{\vt}{\vec{t}} \newcommand{\vu}{\vec{u}} \newcommand{\vv}{\vec{v}} \newcommand{\vw}{\vec{w}} \newcommand{\vx}{\vec{x}} \newcommand{\vy}{\vec{y}} \newcommand{\vz}{\vec{z}} \newcommand{\vpi}{\vecalt{\pi}}$

# Homework 1

## Problem 1: Norms

a) Show that $f(\vx) = \normof{\mA \vx}_p$ is a vector-norm, where $\mA$ is a non-singular matrix.

Solution Because $\mA$ is non-singular, $\mA \vx = 0$ implies that $\vx = 0$. Consequently, by the standard properties of a norm, we know that $f(\vx) \ge 0$, and $f(\vx) = 0$ if and only $\vx = 0$. The other two properties follow immediately from the properties of the vector norms and the properties of matrix multiplication.

b) Show that $f(\vx) = \normof{\mA \vx}_p$ is not a vector-norm if $\mA$ is singular.

Solution When $\mA$ is singular, there is a vector $\vx$ such that $\mA \vx = 0$. This vector violates the first property of being a norm.

These norms will arise in our study of spectral graph theorem. In those cases, the matrix $\mA$ is usually the diagonal matrix of degrees for each node – commonly written $\mD$.

## Problem 2

There are a tremendous number of matrix norms that arise. An interesting class are called the orthgonally invariant norms. Norms in this class satisfy:

for square orthogonal matrices $\mU$ and $\mV$. Recall that a square matrix is orthogonal when $\mU^T \mU = \mI$, i.e. $\mU^{-1} = \mU^T$.

a) Show that $\normof{ \mA }_F$ is orthogonally invariant. (Hint: use the relationship between $\normof{ \mA }_F$ and $\text{trace}( \mA^T \mA )$.)

Solution For the trace operator, $\trace(AB)=\trace(BA)$ so, we have

b) Show that $\normof{ \mA }_2$ is orthogonally invariant. (Hint: first show that $\normof{ \mU \vx }_2 = \normof{\vx}_2$ using the relationshp between $\normof{ \vx }$ and $\vx^T \vx$.)

Solution Note that $\normof{\vx}^2 = \sum_i {x_i}^2 = \vx^T \vx$. Consequently, $\normof{\mU \vx} = \sqrt{\vx^T \mU^T \mU \vx} = \normof{\vx}$.

Hence,

where the second to last expression follows because $\vy$ can be any vector because $\mV$ is a square orthogonal matrix.

## Problem 3

In this problem, we’ll work through the answer to the challenge question on the introductory survey.

Let $\mA$ be the adjacency matrix of a simple, undirected graph.

a) An upper bound on the largest eigenvalue
Show that $\lambda_{\max}(\mA)$ is at most, the maximum degree of the graph. Show that this bound is tight.

Solution $\lambda_{\max} \le \rho(\mA) \le \normof{\mA}$ where $\rho(\mA)$ is the spectral radius, the largest magnitude of any eigenvalue. The bound follows because the 1-norm of the $\mA$ is the largest degree.

Any constant degree graph, e.g. a clique, has this as the largest eigenvalue.

b) A lower bound on the largest eigenvalue Show that $\lambda_{\max}(\mA)$ is at least, the square-root of the maximum degree of the graph. Show that this bound is tight. (Hint: try and find a lower-bound on the Rayleigh-Ritz characterization $\lambda_{\max} = \max \vx^T \mA \vx / \vx^T \vx$.)

Solution Let $\mA_S$ be the adjacency matrix for a graph with fewer edges than $\mA$. Note that

for any vector $\vy$. Let $r$ be the vertex with maximum degree. Set $\mA_S$ to be the adjacency matrix only for the edges that constitute the maximum edgree, then $\mA_S$ is the matrix for a star-graph centered at $r$. Also set

Equivalently, $\vy = \ve_S - (1 - \sqrt{d_{\max}})\ve_r$ (where $\ve_S$ has 1s only on the set of vertices in the star.

Then $$\vy^T \mA_S \vy = \underbrace{\ve_S^T \mA_S \ve_S}_{=2 d_{\max}} - 2 (1 - \sqrt{d_{\max}}) \underbrace{\ve_r^T \mA_S \ve_S}_{=d_{\max}},$$and$$\vy^T \vy = 2d_{\max}$$

by a direct calculation.
Taking these ratios gives the lower-bound of $\sqrt{d_{\max}}$.

## Problem 4

In this question, we’ll show how to use these tools to solve a problem that arose when Amy Langville and I were studying ranking algorithms.

a) the quiz from class Let $\mA$ be an $n \times n$ matrix of all ones:

What are the eigenvalues of $\mA$? What are the eigenvectors for all non-zero eigenvalues? Given a vector $\vx$, how can you tell if it’s in the nullspace (i.e. it’s eigenvector with eigenvalue 0) without looking at the matrix?

Solution The eigenvalues are $n$ and $0$. A null-vector must have sum 0 because the eigenvalue $n$ is associated with the vector of all constants, and all other vectors must be orthogonal, e.g. $\ve^T \vx = 0$ for any vector in the nullspace.

b) my problem with Amy Amy and I were studying the $n \times n$ matrix:

that arose when we were looking at ranking problems like we saw in http://www.cs.purdue.edu/homes/dgleich/nmcomp/lectures/lecture-1-matlab.m What we noticed was that Krylov methods to solve

worked incredibly fast.
Usually this happens when $\mA$ only has a few unique eigenvalues. Show that this is indeed the case. What are the unique eigenvalues of $\mA$?

Note There was a typo in this question. It should have been an $n \times n$ matrix, which makes it non-singular. Anyway, we’ll solve the question as written.

Solution The eigenvalues of this matrix are just a shift away. We start with a single eigenvalue equal to $n+1$, and we shift all the eigenvalues in a positive direction by n+1, e.g. we write $\mA = (n+1) I - \mE$ where $\mE=\ve\ve^T$ is the matrix of all ones.
Hence, we’ll have $n+1$ eigenvalues equal to $n+1$.

c) solving the system Once we realized that there were only a few unique eigenvalues and vectors, we wanted to determine if there was a closed form solution of:

There is such a form. Find it. (By closed form, I mean, given $\vb$, there should be a simple expression for $\vx$.)

Solution If the sum of $\vb$ is non-zero, then there isn’t a solution. i.e. we need $\ve^T \vb = 0$ to have a solution. Now we just have to determine $\vx$ where

Let $\ve^T \vx = \gamma$, then

So we already know that $\vx$ is given by a rescaled $\vb$. Note that $\vx$ is a solution for any value of $\gamma$, so there is an infinite family of solutions. The simplest is just $\vb/(n+1)$.

## Problem 5

In this question, you’ll implement codes to convert between triplet form of a sparse matrix and compressed sparse row.

You may use any language you’d like.

a) Describe and implement a procedure to turn a set of triplet data this data into a one-index based set of arrays: `pointers, columns, and values` for the compressed sparse form of the matrix. Use as little additional memory as possible. (Hint: it’s doable using no extra memory.)

````function [pointers, columns, values] = sparse_compress(m, n, triplets)
% SPARSE_COMPRESS Convert from triplet form
%
% Given a m-by-n sparse matrix stored as triplets:
%   triplets(nzi,:) = (i,j,value)
% Output the  the compressed sparse row arrays for the sparse matrix.

% SOLUTION from https://github.com/dgleich/gaimc/blob/master/sparse_to_csr.m

pointers = zeros(m+1,1);
nz = size(triplets,1);
values = zeros(nz,1);
columns = values(nz,1);
% build pointers for the bucket-sort
for i=1:nz
    pointers(triplets(i,1)+1)=pointers(triplets(i,1)+1)+1;
end
rp=cumsum(rp);
for i=1:nz
    values(pointers(triplets(i,1))+1)=triplets(i,3); 
    columns(pointers(triplets(i,1))+1)=triplets(i,2);
    pointers(triplets(i,1))=pointers(triplets(i,1))+1;
end
for i=n:-1:1
    pointers(i+1)=pointers(i);
end
pointers(1)=0;
pointers=pointers+1;

````

b) Describe and implement a procedure to take in the one-indexed compressed sparse row form of a matrix: `pointers, columns, and values` and the dimensions `m, n` and output the compressed sparse row arrays for the transpose of the matrix:

````function [pointers_out, columns_out, values_out] = sparse_transpose(...
	m, n, pointers, columns, values)
% SPARSE_TRANSPOSE Compute the CSR form of a matrix transpose.
%
% 

triplets = zeros(pointers(end),3);

% SOLUTION
for row=1:m
  for nzi=pointers(row):pointers(row+1)-1
    triplets(nzi,1) = columns(nzi);
    triplets(nzi,2) = row;
    triplets(nzi,3) = values(nzi);
  end
end

[pointers_out, columns_out, values_out] = sparse_compress(n, m, triplets);
````

## Problem 6: Make it run in Matlab/Octave/Scipy/etc.

In this problem, you’ll just have to run three problems on matlab. The first one will be to use the Jacobi method to solve a linear system. The second will be to use a Krylov method to solve a linear system. The third will be to use ARPACK to compute eigenvalues on Matlab.

For this problem, you’ll need to use the ‘minnesota’ road network.
It’s available on the website: http://www.cs.purdue.edu/homes/dgleich/nmcomp/matlab/minnesota.mat The file is in Matlab format. If you need another format, let me know.

a) Use the `gplot` function in Matlab to draw a picture of the Minnesota road network.

Solution

````load minnesota
gplot(A,xy)````

b) Check that the adjacency matrix A has only non-zero values of 1 and that it is symmetric. Fix any problems you encouter.

Solution

````all((nonzeros(A)) == 1)
A = spones(A);
all((nonzeros(A)) == 1)
nnz(A-A')````

c) We’ll do some work with this graph and the linear system described in class:

where $\mL$ is the combinatorial Laplacian matrix.

```` % In Matlab code
 L = diag(sum(A)) - A;
 S = speye(n) - gamma*L;````

For the right-hand side, label all the points above latitude line 47 with 1, and all points below latitude line 44 with -1.

```` % In Matlab code
 b = zeros(n,1);
 b(xy(:,2) > 47) = 1;
 b(xy(:,2) < 44) = -1;````

Write a routine to solve the linear system using the Jacobi method on the compressed sparse row arrays. You should use your code from 5a to get these arrays by calling

```` [src,dst,val] = find(S); 
 T = [src,dst,val];
 [pointers,columns,values] = sparse_compress(size(A,1), size(A,2), T);````

Show the convergence, in the relative residual metric:

when `gamma = 1/7` (Note that $\mA$ is the matrix in the linear system, not the adjacency matrix.)

Show what happens when `gamma=1/5`

Solution (No plots here)

````n = size(A,1);
L = diag(sum(A)) - A;
S = speye(n) - 1/7*L;
b = zeros(n,1);
b(xy(:,2) > 47) = 1;
b(xy(:,2) < 44) = -1;

[i j v] = find(S);
[pointers,columns,values] = sparse_compress(size(S,1), size(S,2),[i,j,v])
[x,resvec]=jacobi(pointers,columns,values,b);
semilogy(resvec);````

Jacobi sketch

````function [x,resvec] = jacobi(pointers,columns,values,b,tol,maxiter)
x = zeros(n,1);
for i=1:maxiter
  y = zeros(n,1);
  for row=1:length(b)
    yi = b(row); di = 0;
    for nzi=pointers(row):pointers(row+1)-1
      if columns(nzi) ~= row, yi = yi - values(nzi)*x(columns(nzi)); 
      else di=values(nzi); 
      end 
    end
    y(row) = yi/di;
  end
  % compute the residual
  r = zeros(n,1);
  for row=1:length(b)
    ri = b(row);
    for nzi=pointers(row):pointers(row+1)-1
      ri = ri - values(nzi)*y(columns(nzi));
    end
  end
  resvec(i)=norm(ri);
  if resvec(i) < tol, break; end
end
resvec = resvec(1:i);
if resvec(end) > tol, warning('did not converge'); end````

d) Try using Conjugate Gradient `pcg` and `minres` in Matlab on this same system with `gamma=1/7` and `gamma=1/5`. Show the convergence of the residuals.

Solution Both work for gamma=1/7, neither work for gamma=1/5.

````
S = speye(n) - 1/5*L;
b = zeros(n,1);
b(xy(:,2) > 47) = 1;
b(xy(:,2) < 44) = -1;

%%
[x,flag,relres,iter,resvec] = pcg(S,b);
semilogy(resvec);

%%
[x,flag,relres,iter,resvec] = minres(S,b,1e-8,500);
semilogy(resvec);
````

The `semilogy` was how to show the convergence.

e) Use the `eigs` routine to find the 18 smallest eigenvalues of the Laplacian matrix $\mL$.

````>> [V,D] = eigs(L,18,'SA'); diag(D)

ans =

   -0.0000
    0.0000
    0.0008
    0.0021
    0.0023
    0.0031
    0.0051
    0.0055
    0.0068
    0.0073
    0.0100
    0.0116
    0.0123
    0.0126
    0.0134
    0.0151
    0.0165
    0.0167````
{"extraction_info": {"found_math": true, "script_math_tex": 83, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "github"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "english_confidence": 0.8034659028053284, "perplexity_flag": "middle"}