A basic introduction to defining and manipulating matrices is given
here. It is assumed that you know the basics on how to define and
manipulate vectors (*Introduction to Vectors in Matlab*) using matlab.

Defining a matrix is similar to defining a vector (*Introduction to Vectors in Matlab*). To define a
matrix, you can treat it like a column of row vectors (note that the
spaces are required!):

```
>> A = [ 1 2 3; 3 4 5; 6 7 8]
A =
1 2 3
3 4 5
6 7 8
```

You can also treat it like a row of column vectors:

```
>> B = [ [1 2 3]' [2 4 7]' [3 5 8]']
B =
1 2 3
2 4 5
3 7 8
```

(Again, it is important to include the spaces.)

If you have been putting in variables through this and the tutorial on
vectors (*Introduction to Vectors in Matlab*), then you probably have a lot of variables
defined. If you lose track of what variables you have defined, the
*whos* command will let you know all of the variables you have in your
work space.

```
>> whos
Name Size Bytes Class
A 3x3 72 double array
B 3x3 72 double array
v 1x5 40 double array
Grand total is 23 elements using 184 bytes
```

We assume that you are doing this tutorial after completing the
previous tutorial. The vector **v** was defined in the previous
tutorial.

As mentioned before, the notation used by Matlab is the standard linear algebra notation you should have seen before. Matrix-vector multiplication can be easily done. You have to be careful, though, your matrices and vectors have to have the right size!

```
>> v = [0:2:8]
v =
0 2 4 6 8
>> A*v(1:3)
??? Error using ==> *
Inner matrix dimensions must agree.
>> A*v(1:3)'
ans =
16
28
46
```

Get used to seeing that particular error message! Once you start throwing matrices and vectors around, it is easy to forget the sizes of the things you have created.

You can work with different parts of a matrix, just as you can with vectors. Again, you have to be careful to make sure that the operation is legal.

```
>> A(1:2,3:4)
??? Index exceeds matrix dimensions.
>> A(1:2,2:3)
ans =
2 3
4 5
>> A(1:2,2:3)'
ans =
2 4
3 5
```

Once you are able to create and manipulate a matrix, you can perform many standard operations on it. For example, you can find the inverse of a matrix. You must be careful, however, since the operations are numerical manipulations done on digital computers. In the example, the matrix A is not a full matrix, but matlab’s inverse routine will still return a matrix.

```
>> inv(A)
Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 4.565062e-18
ans =
1.0e+15 *
-2.7022 4.5036 -1.8014
5.4043 -9.0072 3.6029
-2.7022 4.5036 -1.8014
```

By the way, Matlab is case sensitive. This is another potential source of problems when you start building complicated algorithms.

```
>> inv(a)
??? Undefined function or variable a.
```

Other operations include finding an approximation to the eigen values
of a matrix. There are two versions of this routine, one just finds
the eigen values, the other finds both the eigen values and the eigen
vectors. If you forget which one is which, you can get more
information by typing *help eig* at the matlab prompt.

```
>> eig(A)
ans =
14.0664
-1.0664
0.0000
>> [v,e] = eig(A)
v =
-0.2656 0.7444 -0.4082
-0.4912 0.1907 0.8165
-0.8295 -0.6399 -0.4082
e =
14.0664 0 0
0 -1.0664 0
0 0 0.0000
>> diag(e)
ans =
14.0664
-1.0664
0.0000
```

There are also routines that let you find solutions to equations. For
example, if *A* **x** *=* **b** and you want to find **x**, a slow way
to find **x** is to simply invert *A* and perform a left multiply on
both sides (more on that later). It turns out that there are more
efficient and more stable methods to do this (L/U decomposition with
pivoting, for example). Matlab has special commands that will do this
for you.

Before finding the approximations to linear systems, it is important
to remember that if *A* and *B* are both matrices, then *AB* is not
necessarily equal to *BA*. To distinguish the difference between
solving systems that have a right or left multiply, Matlab uses two
different operators, */* and *\*. Examples of their use are given
below. It is left as an exercise for you to figure out which one is
doing what.

```
>> v = [1 3 5]'
v =
1
3
5
>> x = A\v
Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 4.565062e-18
x =
1.0e+15 *
1.8014
-3.6029
1.8014
>> x = B\v
x =
2
1
-1
>> B*x
ans =
1
3
5
>> x1 = v'/B
x1 =
4.0000 -3.0000 1.0000
>> x1*B
ans =
1.0000 3.0000 5.0000
```

Finally, sometimes you would like to clear all of your data and start
over. You do this with the *clear* command. Be careful though, it does
not ask you for a second opinion and its results are final .

```
>> clear
>> whos
```

Matlab Tutorial by Kelly Black is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Based on a work at http://www.cyclismo.org/tutorial/matlab/.

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