19.2 Broadcasting

Broadcasting refers to how Octave binary operators and functions behave when their matrix or array operands or arguments differ in size. Since version 3.6.0, Octave now automatically broadcasts vectors, matrices, and arrays when using elementwise binary operators and functions. Broadly speaking, smaller arrays are “broadcast” across the larger one, until they have a compatible shape. The rule is that corresponding array dimensions must either

1. be equal or,
2. one of them must be 1.

In case all dimensions are equal, no broadcasting occurs and ordinary element-by-element arithmetic takes place. For arrays of higher dimensions, if the number of dimensions isn't the same, then missing trailing dimensions are treated as 1. When one of the dimensions is 1, the array with that singleton dimension gets copied along that dimension until it matches the dimension of the other array. For example, consider

     x = [1 2 3;
          4 5 6;
          7 8 9];
     
     y = [10 20 30];
     
     x + y

Without broadcasting, x + y would be an error because dimensions do not agree. However, with broadcasting it is as if the following operation were performed:

     x = [1 2 3
          4 5 6
          7 8 9];
     
     y = [10 20 30
          10 20 30
          10 20 30];
     
     x + y
     ⇒    11   22   33
           14   25   36
           17   28   39

That is, the smaller array of size [1 3] gets copied along the singleton dimension (the number of rows) until it is [3 3]. No actual copying takes place, however. The internal implementation reuses elements along the necessary dimension in order to achieve the desired effect without copying in memory.

Both arrays can be broadcast across each other, for example, all pairwise differences of the elements of a vector with itself:

     y - y'
     ⇒    0   10   20
         -10    0   10
         -20  -10    0

Here the vectors of size [1 3] and [3 1] both get broadcast into matrices of size [3 3] before ordinary matrix subtraction takes place.

For a higher-dimensional example, suppose img is an RGB image of size [m n 3] and we wish to multiply each colour by a different scalar. The following code accomplishes this with broadcasting,

     img .*= permute ([0.8, 0.9, 1.2], [1, 3, 2]);

Note the usage of permute to match the dimensions of the [0.8, 0.9, 1.2] vector with img.

For functions that are not written with broadcasting semantics, bsxfun can be useful for coercing them to broadcast.

Broadcasting is only applied if either of the two broadcasting conditions hold. As usual, however, broadcasting does not apply when two dimensions differ and neither is 1:

     x = [1 2 3
          4 5 6];
     y = [10 20
          30 40];
     x + y

This will produce an error about nonconformant arguments.

Besides common arithmetic operations, several functions of two arguments also broadcast. The full list of functions and operators that broadcast is

           plus      +  .+
           minus     -  .-
           times     .*
           rdivide   ./
           ldivide   .\
           power     .^  .**
           lt        <
           le        <=
           eq        ==
           gt        >
           ge        >=
           ne        !=  ~=
           and       &
           or        |
           atan2
           hypot
           max
           min
           mod
           rem
           xor
     
           +=  -=  .+=  .-=  .*=  ./=  .\=  .^=  .**=  &=  |=

Beware of resorting to broadcasting if a simpler operation will suffice. For matrices a and b, consider the following:

     c = sum (permute (a, [1, 3, 2]) .* permute (b, [3, 2, 1]), 3);

This operation broadcasts the two matrices with permuted dimensions across each other during elementwise multiplication in order to obtain a larger 3d array, and this array is the summed along the third dimension. A moment of thought will prove that this operation is simply the much faster ordinary matrix multiplication, c = a*b;.

A note on terminology: “broadcasting” is the term popularized by the Numpy numerical environment in the Python programming language. In other programming languages and environments, broadcasting may also be known as binary singleton expansion (BSX, in Matlab, and the origin of the name of the bsxfun function), recycling (R programming language), single-instruction multiple data (SIMD), or replication.

19.2.1 Broadcasting and Legacy Code

The new broadcasting semantics do not affect almost any code that worked in previous versions of Octave without error. Thus for example all code inherited from Matlab that worked in previous versions of Octave should still work without change in Octave. The only exception is code such as

     try
       c = a.*b;
     catch
       c = a.*a;
     end_try_catch

that may have relied on matrices of different size producing an error. Due to how broadcasting changes semantics with older versions of Octave, by default Octave warns if a broadcasting operation is performed. To disable this warning, refer to its ID (see doc-warning_ids):

     warning ("off", "Octave:broadcast");

If you want to recover the old behaviour and produce an error, turn this warning into an error:

     warning ("error", "Octave:broadcast");