Octave Programming Tutorial/Vectorization

Writing routines in terms of vector operations can be orders of magnitudes more efficient than using built-in interpreted loops because Octave can make use of highly optimized FORTRAN and C numerical linear algebra libraries instead. Even if a routine or function is not written in a vectorized form, it is possible to take advantage of vectorization by using arrayfun or a similar structure.

vectorizing a regular function with arrayfun
Consider an anonymous function octave:1> f = @(x) sin(x)*x

Octave output : f = @(x) sin (x)*x and assume that we want to calculate this function for every element of a given vector of integers from 1 to 7 : octave:2> y=1:7

y = 1  2   3   4   5   6   7 then passing y as an argument for f will give error octave:3> f(y) error: operator *: nonconformant arguments (op1 is 1x7, op2 is 1x7) error: called from: error:   at line -1, column -1 this is because f is not defined for a vector input. But this is not a problem as we can do: octave:4> arrayfun(f,y)

and output is : ans = 0.84147  1.81859   0.42336  -3.02721  -4.79462  -1.67649   4.59891 This is an order of magnitude faster than calculating f for many y values by using a loop which has a big overhead.

GNU Octave/Wydajnosc