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Techniques for Improving Performance

This section covers the following suggestions on how you can improve the performance of your MATLAB programs:

Vectorizing Loops

MATLAB is a matrix language, which means it is designed for vector and matrix operations. You can often speed up your M-file code by using vectorizing algorithms that take advantage of this design. Vectorization means converting for and while loops to equivalent vector or matrix operations.

Simple Example of Vectorizing

Here is one way to compute the sine of 1001 values ranging from 0 to 10:

A vectorized version of the same code is

The second example executes much faster than the first and is the way MATLAB is meant to be used. Test this on your system by creating M-file scripts that contain the code shown, and then using the tic and toc functions to time the M-files.

Advanced Example of Vectorizing

repmat is an example of a function that takes advantage of vectorization. It accepts three input arguments: an array A, a row dimension M, and a column dimension N.

repmat creates an output array that contains the elements of array A, replicated and "tiled" in an M-by-N arrangement:

repmat uses vectorization to create the indices that place elements in the output array:

Step 1, above, obtains the row and column sizes of the input array.

Step 2 creates two column vectors. mind contains the integers from 1 through the row size of A. The nind variable contains the integers from 1 through the column size of A.

Step 3 uses a MATLAB vectorization trick to replicate a single column of data through any number of columns. The code is

where nCols is the desired number of columns in the resulting matrix.

Step 4 uses array indexing to create the output array. Each element of the row index array, mind, is paired with each element of the column index array, nind, using the following procedure:

  1. The first element of mind, the row index, is paired with each element of nind. MATLAB moves through the nind matrix in a columnwise fashion, so mind(1,1) goes with nind(1,1), and then nind(2,1), and so on. The result fills the first row of the output array.
  2. Moving columnwise through mind, each element is paired with the elements of nind as above. Each complete pass through the nind matrix fills one row of the output array.

Functions Used in Vectorizing

Some of the most commonly used functions for vectorizing are as follows

Test to determine if all elements are nonzero
Test for any nonzeros
Find cumulative sum
Find differences and approximate derivatives
Find indices and values of nonzero elements
Convert from linear index to subscripts
Inverse permute dimensions of a multidimensional array
Convert numeric values to logical
Generate arrays for multidimensional functions and interpolation
Rearrange dimensions of a multidimensional array
Find product of array elements
Replicate and tile an array
Change the shape of an array
Shift array dimensions
Sort array elements in ascending or descending order
Remove singleton dimensions from an array
Convert from subscripts to linear index
Find the sum of array elements

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