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MATLAB^{®} is optimized for operations involving matrices and
vectors. The process of revising loop-based, scalar-oriented code
to use MATLAB matrix and vector operations is called *vectorization*.
Vectorizing your code is worthwhile for several reasons:

*Appearance*: Vectorized mathematical code appears more like the mathematical expressions found in textbooks, making the code easier to understand.*Less Error Prone*: Without loops, vectorized code is often shorter. Fewer lines of code mean fewer opportunities to introduce programming errors.*Performance*: Vectorized code often runs much faster than the corresponding code containing loops.

This code computes the sine of 1,001 values ranging from 0 to 10:

i = 0; for t = 0:.01:10 i = i + 1; y(i) = sin(t); end

This is a vectorized version of the same code:

t = 0:.01:10; y = sin(t);

The second code sample usually executes faster than the first
and is a more efficient use of MATLAB. Test execution speed on
your system by creating scripts that contain the code shown, and then
use the `tic`

and `toc`

functions
to measure their execution time.

This code computes the cumulative sum of a vector at every fifth element:

x = 1:10000; ylength = (length(x) - mod(length(x),5))/5; y(1:ylength) = 0; for n= 5:5:length(x) y(n/5) = sum(x(1:n)); end

Using vectorization, you can write a much more concise MATLAB process. This code shows one way to accomplish the task:

x = 1:10000; xsums = cumsum(x); y = xsums(5:5:length(x));

Array operators perform the same operation for all elements in the data set. These
types of operations are useful for repetitive calculations. For example, suppose you
collect the volume (`V`

) of various cones by recording their
diameter (`D`

) and height (`H`

). If you collect
the information for just one cone, you can calculate the volume for that single
cone:

V = 1/12*pi*(D^2)*H;

Now, collect information on 10,000 cones. The vectors `D`

and
`H`

each contain 10,000 elements, and you want to calculate
10,000 volumes. In most programming languages, you need to set up a loop similar to
this MATLAB
code:

for n = 1:10000 V(n) = 1/12*pi*(D(n)^2)*H(n); end

With MATLAB, you can perform the calculation for each element of a vector with similar syntax as the scalar case:

```
% Vectorized Calculation
V = 1/12*pi*(D.^2).*H;
```

**Note**

Placing a period (`.`

) before the operators
`*`

, `/`

, and `^`

,
transforms them into array operators.

Array operators also enable you to combine matrices of different dimensions. This automatic expansion of size-1 dimensions is useful for vectorizing grid creation, matrix and vector operations, and more.

Suppose that matrix `A`

represents test scores, the rows of which
denote different classes. You want to calculate the difference between the average
score and individual scores for each class. Using a loop, the operation looks
like:

A = [97 89 84; 95 82 92; 64 80 99;76 77 67;... 88 59 74; 78 66 87; 55 93 85]; mA = mean(A); B = zeros(size(A)); for n = 1:size(A,2) B(:,n) = A(:,n) - mA(n); end

A more direct way to do this is with `A - mean(A)`

, which avoids
the need of a loop and is significantly faster.

devA = A - mean(A)

devA = 18 11 0 16 4 8 -15 2 15 -3 -1 -17 9 -19 -10 -1 -12 3 -24 15 1

Even though `A`

is a 7-by-3 matrix and `mean(A)`

is a 1-by-3 vector, MATLAB implicitly expands the vector as if it had the same size as the
matrix, and the operation executes as a normal element-wise minus operation.

The size requirement for the operands is that for each dimension, the arrays must either have the same size or one of them is 1. If this requirement is met, then dimensions where one of the arrays has size 1 are expanded to be the same size as the corresponding dimension in the other array. For more information, see Compatible Array Sizes for Basic Operations.

Another area where implicit expansion is useful for vectorization is if you are
working with multidimensional data. Suppose you want to evaluate a function,
`F`

, of two variables, `x`

and
`y`

.

`F(x,y) = x*exp(-x`

^{2} -
y^{2})

To evaluate this function at every combination of points in the
`x`

and `y`

vectors, you need to define a grid
of values. For this task you should avoid using loops to iterate through the point
combinations. Instead, if one of the vectors is a column and the other is a row,
then MATLAB automatically constructs the grid when the vectors are used with an
array operator, such as `x+y`

or `x-y`

. In this
example, `x`

is a 21-by-1 vector and `y`

is a
1-by-16 vector, so the operation produces a 21-by-16 matrix by expanding the second
dimension of `x`

and the first dimension of
`y`

.

x = (-2:0.2:2)'; % 21-by-1 y = -1.5:0.2:1.5; % 1-by-16 F = x.*exp(-x.^2-y.^2); % 21-by-16

In cases where you want to explicitly create the grids, you can use the `meshgrid`

and `ndgrid`

functions.

A logical extension of the bulk processing of arrays is to vectorize comparisons and decision making. MATLAB comparison operators accept vector inputs and return vector outputs.

For example, suppose while collecting data from 10,000 cones,
you record several negative values for the diameter. You can determine
which values in a vector are valid with the `>=`

operator:

D = [-0.2 1.0 1.5 3.0 -1.0 4.2 3.14]; D >= 0

ans = 0 1 1 1 0 1 1

`Vgood`

, for which the corresponding
elements of `D`

are nonnegative:Vgood = V(D >= 0);

MATLAB allows you to perform a logical AND or OR on the
elements of an entire vector with the functions `all`

and `any`

,
respectively. You can throw a warning if all values of `D`

are
below zero:

if all(D < 0) warning('All values of diameter are negative.') return end

MATLAB can also compare two vectors with compatible sizes, allowing you to
impose further restrictions. This code finds all the values where V is nonnegative
and `D`

is greater than
`H`

:

V((V >= 0) & (D > H))

To aid comparison, MATLAB contains special values to denote overflow, underflow, and undefined
operators, such as `Inf`

and `NaN`

. Logical
operators `isinf`

and `isnan`

exist to help
perform logical tests for these special values. For example, it is often useful to
exclude `NaN`

values from
computations:

x = [2 -1 0 3 NaN 2 NaN 11 4 Inf]; xvalid = x(~isnan(x))

xvalid = 2 -1 0 3 2 11 4 Inf

**Note**

`Inf == Inf`

returns true; however, ```
NaN
== NaN
```

always returns false.

When vectorizing code, you often need to construct a matrix with a particular size or structure. Techniques exist for creating uniform matrices. For instance, you might need a 5-by-5 matrix of equal elements:

A = ones(5,5)*10;

v = 1:5; A = repmat(v,3,1)

A = 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5

The function `repmat`

possesses flexibility in building
matrices from smaller matrices or vectors. `repmat`

creates
matrices by repeating an input matrix:

A = repmat(1:3,5,2) B = repmat([1 2; 3 4],2,2)

A = 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 B = 1 2 1 2 3 4 3 4 1 2 1 2 3 4 3 4

In many applications, calculations done on an element of a vector
depend on other elements in the same vector. For example, a vector, *x*,
might represent a set. How to iterate through a set without a `for`

or `while`

loop
is not obvious. The process becomes much clearer and the syntax less
cumbersome when you use vectorized code.

A number of different ways exist for finding the redundant elements of a vector. One way
involves the function `diff`

. After sorting the vector
elements, equal adjacent elements produce a zero entry when you use the
`diff`

function on that vector. Because
`diff(x)`

produces a vector that has one fewer element
than `x`

, you must add an element that is not equal to any
other element in the set. `NaN`

always satisfies this
condition. Finally, you can use logical indexing to choose the unique elements
in the
set:

x = [2 1 2 2 3 1 3 2 1 3]; x = sort(x); difference = diff([x,NaN]); y = x(difference~=0)

y = 1 2 3

`unique`

function:y=unique(x);

`unique`

function might provide more functionality than
is needed and slow down the execution of your code. Use the
`tic`

and `toc`

functions if you want
to measure the performance of each code snippet.Rather than merely returning the set, or subset, of `x`

,
you can count the occurrences of an element in a vector. After the
vector sorts, you can use the `find`

function to
determine the indices of zero values in `diff(x)`

and
to show where the elements change value. The difference between subsequent
indices from the `find`

function indicates the
number of occurrences for a particular element:

x = [2 1 2 2 3 1 3 2 1 3]; x = sort(x); difference = diff([x,max(x)+1]); count = diff(find([1,difference])) y = x(find(difference))

count = 3 4 3 y = 1 2 3

`find`

function does not return
indices for `NaN`

elements. You can count the number
of `NaN`

and `Inf`

values using
the `isnan`

and `isinf`

functions.count_nans = sum(isnan(x(:))); count_infs = sum(isinf(x(:)));

Function | Description |
---|---|

`all` | Determine if all array elements are nonzero or true |

`any` | Determine if any array elements are nonzero |

`cumsum` | Cumulative sum |

`diff` | Differences and Approximate Derivatives |

`find` | Find indices and values of nonzero elements |

`ind2sub` | Subscripts from linear index |

`ipermute` | Inverse permute dimensions of N-D array |

`logical` | Convert numeric values to logicals |

`meshgrid` | Rectangular grid in 2-D and 3-D space |

`ndgrid` | Rectangular grid in N-D space |

`permute` | Rearrange dimensions of N-D array |

`prod` | Product of array elements |

`repmat` | Repeat copies of array |

`reshape` | Reshape array |

`shiftdim` | Shift dimensions |

`sort` | Sort array elements |

`squeeze` | Remove singleton dimensions |

`sub2ind` | Convert subscripts to linear indices |

`sum` | Sum of array elements |