Network & Matrix Computations

David Gleich

Purdue University

Fall 2011

Course number CS 59000-NMC

Tuesdays and Thursday, 10:30am-11:45am

CIVL 2123

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Homework 1

Due September 20th, 2011

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:

$$\normof{\mA} = \normof{\mU \mA \mV}$$

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

$$\normof{\mU \mA \mV}_F^2 = \trace(\mV^T \mA^T \mU^T \mU \mA \mV) = \trace(\mV^T (\mA^T \mA \mV)) = \trace((\mA^T \mA \mV) \mV^T) = \trace(\mA^T \mA) = \normof{\mA}_F^2.$$

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,

$$\normof{\mU \mA \mV^T}_2 = \max_{\vx} \frac{\normof{\mU \mA \mV^T \vx}_2}{\normof{\vx}_2} = \max_{\vx} \frac{\normof{\mA \mV^T \vx}_2}{\normof{\vx}_2} = \max_{\vx} \frac{\normof{\mA \mV^T \vx}_2}{\normof{\mV^T \vx}_2} = \max_{\vy=\mV^T\vx} \frac{\normof{\mA \vy}_2}{\normof{\vy}_2} = \normof{\mA}_2$$

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

$$\lambda_{\max} = \max_{\vx} \vx^T \mA \vx / (\vx^T \vx) \ge \max_{\vx} \vx^T \mA_S \vx/ (\vx^T \vx) \ge \vy^T \mA_S \vy /(\vy^T \vy).$$

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

$$[\vy]_i = \begin{cases} 0 & {i \not= r, i \not\in \Gamma(r)} \\ \sqrt{d_{\max}} & i = r \\ 1 & i \in \Gamma(r) \end{cases}.$$

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:

$$\mA = \bmat{ 1 & \cdots & 1 \\ \vdots & & \vdots \\ 1 & \cdots & 1 }.$$

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:

$$\mA = \bmat{ n & -1 & \cdots & -1 \\ -1 & \ddots & & \vdots \\ \vdots & & \ddots & -1 \\ -1 & \cdots & -1 & n }$$

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

$$\mA \vx = \vb$$

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:

$$\mA \vx = \vb.$$

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

$$[(n+1) \mI - \ve \ve^T ] \vx = \vb.$$

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

$$\vx = (\vb - \gamma \ve)/(n+1).$$

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:

$$\mI - \gamma \mL$$

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:

$$\normof{\vb - \mA \vx^{(k)}}/\normof{b}$$

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