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A *matrix* is just a vector of vectors, all the same length.
This means you can enter a matrix using nested brackets. You can
also use the semicolon character to enter a matrix. We’ll show
both methods here:

```
1: [ [ 1, 2, 3 ] 1: [ [ 1, 2, 3 ]
[ 4, 5, 6 ] ] [ 4, 5, 6 ] ]
. .
[[1 2 3] [4 5 6]] ' [1 2 3; 4 5 6] RET
```

We’ll be using this matrix again, so type `s 4` to save it now.

Note that semicolons work with incomplete vectors, but they work better in algebraic entry. That’s why we use the apostrophe in the second example.

When two matrices are multiplied, the lefthand matrix must have
the same number of columns as the righthand matrix has rows.
Row ‘`i`’, column ‘`j`’ of the result is effectively the
dot product of row ‘`i`’ of the left matrix by column ‘`j`’
of the right matrix.

If we try to duplicate this matrix and multiply it by itself, the dimensions are wrong and the multiplication cannot take place:

```
1: [ [ 1, 2, 3 ] * [ [ 1, 2, 3 ]
[ 4, 5, 6 ] ] [ 4, 5, 6 ] ]
.
RET *
```

Though rather hard to read, this is a formula which shows the product
of two matrices. The ‘`*`’ function, having invalid arguments, has
been left in symbolic form.

We can multiply the matrices if we *transpose* one of them first.

```
2: [ [ 1, 2, 3 ] 1: [ [ 14, 32 ] 1: [ [ 17, 22, 27 ]
[ 4, 5, 6 ] ] [ 32, 77 ] ] [ 22, 29, 36 ]
1: [ [ 1, 4 ] . [ 27, 36, 45 ] ]
[ 2, 5 ] .
[ 3, 6 ] ]
.
U v t * U TAB *
```

Matrix multiplication is not commutative; indeed, switching the order of the operands can even change the dimensions of the result matrix, as happened here!

If you multiply a plain vector by a matrix, it is treated as a single row or column depending on which side of the matrix it is on. The result is a plain vector which should also be interpreted as a row or column as appropriate.

2: [ [ 1, 2, 3 ] 1: [14, 32] [ 4, 5, 6 ] ] . 1: [1, 2, 3] . r 4 r 1 *

Multiplying in the other order wouldn’t work because the number of rows in the matrix is different from the number of elements in the vector.

(•) **Exercise 1.** Use ‘`*`’ to sum along the rows
of the above
2x3
matrix to get ‘`[6, 15]`’. Now use ‘`*`’ to sum along the columns
to get ‘`[5, 7, 9]`’.
See 1. (•)

An *identity matrix* is a square matrix with ones along the
diagonal and zeros elsewhere. It has the property that multiplication
by an identity matrix, on the left or on the right, always produces
the original matrix.

```
1: [ [ 1, 2, 3 ] 2: [ [ 1, 2, 3 ] 1: [ [ 1, 2, 3 ]
[ 4, 5, 6 ] ] [ 4, 5, 6 ] ] [ 4, 5, 6 ] ]
. 1: [ [ 1, 0, 0 ] .
[ 0, 1, 0 ]
[ 0, 0, 1 ] ]
.
r 4 v i 3 RET *
```

If a matrix is square, it is often possible to find its *inverse*,
that is, a matrix which, when multiplied by the original matrix, yields
an identity matrix. The `&` (reciprocal) key also computes the
inverse of a matrix.

1: [ [ 1, 2, 3 ] 1: [ [ -2.4, 1.2, -0.2 ] [ 4, 5, 6 ] [ 2.8, -1.4, 0.4 ] [ 7, 6, 0 ] ] [ -0.73333, 0.53333, -0.2 ] ] . . r 4 r 2 | s 5 &

The vertical bar `|` *concatenates* numbers, vectors, and
matrices together. Here we have used it to add a new row onto
our matrix to make it square.

We can multiply these two matrices in either order to get an identity.

1: [ [ 1., 0., 0. ] 1: [ [ 1., 0., 0. ] [ 0., 1., 0. ] [ 0., 1., 0. ] [ 0., 0., 1. ] ] [ 0., 0., 1. ] ] . . M-RET * U TAB *

Matrix inverses are related to systems of linear equations in algebra. Suppose we had the following set of equations:

a + 2b + 3c = 6 4a + 5b + 6c = 2 7a + 6b = 3

This can be cast into the matrix equation,

[ [ 1, 2, 3 ] [ [ a ] [ [ 6 ] [ 4, 5, 6 ] * [ b ] = [ 2 ] [ 7, 6, 0 ] ] [ c ] ] [ 3 ] ]

We can solve this system of equations by multiplying both sides by the inverse of the matrix. Calc can do this all in one step:

2: [6, 2, 3] 1: [-12.6, 15.2, -3.93333] 1: [ [ 1, 2, 3 ] . [ 4, 5, 6 ] [ 7, 6, 0 ] ] . [6,2,3] r 5 /

The result is the ‘`[a, b, c]`’ vector that solves the equations.
(Dividing by a square matrix is equivalent to multiplying by its
inverse.)

Let’s verify this solution:

```
2: [ [ 1, 2, 3 ] 1: [6., 2., 3.]
[ 4, 5, 6 ] .
[ 7, 6, 0 ] ]
1: [-12.6, 15.2, -3.93333]
.
r 5 TAB *
```

Note that we had to be careful about the order in which we multiplied
the matrix and vector. If we multiplied in the other order, Calc would
assume the vector was a row vector in order to make the dimensions
come out right, and the answer would be incorrect. If you
don’t feel safe letting Calc take either interpretation of your
vectors, use explicit
Nx1
or
1xN
matrices instead. In this case, you would enter the original column
vector as ‘`[[6], [2], [3]]`’ or ‘`[6; 2; 3]`’.

(•) **Exercise 2.** Algebraic entry allows you to make
vectors and matrices that include variables. Solve the following
system of equations to get expressions for ‘`x`’ and ‘`y`’
in terms of ‘`a`’ and ‘`b`’.

x + a y = 6 x + b y = 10

See 2. (•)

(•) **Exercise 3.** A system of equations is “over-determined”
if it has more equations than variables. It is often the case that
there are no values for the variables that will satisfy all the
equations at once, but it is still useful to find a set of values
which “nearly” satisfy all the equations. In terms of matrix equations,
you can’t solve ‘`A X = B`’ directly because the matrix ‘`A`’
is not square for an over-determined system. Matrix inversion works
only for square matrices. One common trick is to multiply both sides
on the left by the transpose of ‘`A`’:
‘`trn(A)*A*X = trn(A)*B`’.
Now
‘`trn(A)*A`’
is a square matrix so a solution is possible. It turns out that the
‘`X`’ vector you compute in this way will be a “least-squares”
solution, which can be regarded as the “closest” solution to the set
of equations. Use Calc to solve the following over-determined
system:

a + 2b + 3c = 6 4a + 5b + 6c = 2 7a + 6b = 3 2a + 4b + 6c = 11

See 3. (•)