UNB/ CS/ David Bremner/ teaching/ cs2613/ labs/ Lab 19


Most of the material here is covered in the Gnu Octave Beginner's Guide, in particular

For some parts, we will refer to the GNU Octave Manual

We'll refer to the online text by Robert Beezer for linear algebra background. We can happily ignore the stuff about complex numbers.

Before the lab

One of the main features of Octave we will discuss is vectorization. To understand it, we need some background material on Linear Algebra. If you've taken a linear algebra course recently, this should be easy, otherwise you will probably need to review

If you don't have time before this lab, make sure you review any needed linear algebra before the next lab.

Running Octave


15 minutes
Demo/Group programming.

Let's dive in to programming in Octave with a straight-forward port of our Python Fibonacci function. Save the following in ~/fcshome/cs2613/labs/L19/fib.m. It turns out to be important that the function name is the same as the file name.

function ret = fib(n)
  a = 0;
  b = 1;
  for i=0:n


%!assert (fib(0) == 0);
%!assert (fib(1) == 1);
%!assert (fib(2) == 1);
%!assert (fib(3) == 2);
%!assert (fib(4) == 3);
%!assert (fib(5) == 5);

Fibonaccci as matrix multiplication

20 minutes

The following is a well known identity about the Fibonacci numbers F(i).

[ 1, 1;
  1, 0 ]^n = [ F(n+1), F(n);
               F(n),   F(n-1) ]

Since matrix exponentiation is built-in to octave, this is particularly easy to implement in octave

Save the following as ~/fcshome/cs2613/labs/L19/fibmat.m, fill in the two matrix operations needed to complete the algorithm

function ret = fibmat(n)
  A = [1,1; 1,0];


%!assert (fibmat(0) == 0);
%!assert (fibmat(1) == 1);
%!assert (fibmat(2) == 1);
%!assert (fibmat(3) == 2);
%!assert (fibmat(4) == 3);
%!assert (fibmat(5) == 5);
%!assert (fibmat(6) == 8);
%!assert (fibmat(25) == 75025);

Performance comparison

10 minutes
Demo / discussion

We can expect the second Fibonacci implementation to be faster for two distinct reasons

Of course, the first rule of performance tuning is to carefully test any proposed improvement. The following code gives an extensible way to run simple timing tests, in a manner analogous to the Python timeit method, whose name it borrows.

# Based on an example from the Julia microbenchmark suite.

function timeit(func, argument, reps)
    times = zeros(reps, 1);

    for i=1:reps
      tic(); func(argument); times(i) = toc();

    times = sort(times);
    fprintf ('%s\tmedian=%.3fms mean=%.3fms total=%.3fms\n',func2str(func), median(times)*1000,
             mean(times)*1000, sum(times)*1000);

What are the new features of octave used in this sample code?

We can either use timeit from the octave command line, or build a little utility function like

function bench
  timeit(@fib, 42, 100000)
  timeit(@fibmat, 42, 100000)

Python Quiz