## Protected: Linear Algebra, Section 6.2

## Protected: Calculus 3, Section 5.5

## Protected: Linear Algebra, Section 6.1

## Protected: Calculus 3, Section 5.4

## Protected: Linear Algebra – Complex Eigenvalues

## Protected: Calculus 3, Section 5.3

## Protected: Calculus 3, Section 5.2

## Linear Algebra – First Matrix Computations with Sage

Here is a short overview for doing matrix manipulations online using the Sage framework. For more information, see my Sage information page.

For short computations, I recommend using the SageCellServer. Here you can type in a few lines of code, hit “evaluate”, and see the result.

To get started, try this code:

2+3

When you hit “evaluate” you should see the result: `5`

.

Now try this code:

A = matrix([[1,2],[3,4]]) show(A)

The first line tells the computer what `A`

is. The second line tells the computer to show you what `A`

is. What should happen if you only put in the first line? Try it out?

Now let’s compute the inverse of matrix `A`

. We use the following code.

A = matrix([[1,2],[3,4]]) B = A.inverse() show(B)

Line 1 defines the matrix `A`

.

Line 2 defines the matrix `B`

as being equal to the inverse of `A`

.

The way we do this is by attaching the inverse function to the matrix `A`

. The dot (period) is the “attaching” operation in Sage.

Line 3 shows the matrix `B`

.

Here is a more “efficient” way to do the same thing:

A = matrix([[1,2],[3,4]]) show( A.inverse() )

Here is a more complicated piece of code:

S = matrix([[1,-1],[2,2]]) D = matrix([[2,0],[0,3]]) A = S*D*S.inverse() show(A)

Let’s unpack what this is doing:

– Line 1 defines a change of coordinate matrix

– Line 2 defines a diagonal matrix that is the matrix of our transformation in the basis determined by the columns of .

– Line 3 defines

– Line 4 shows us the matrix .

If you wanted Sage to show the matrix , what would you do?

Finally, let me mention the `.eigenvectors_right()`

command. This command takes in a matrix and gives out a list of:

– eigenvalues, eigenvectors, and the multiplicity

For example:

S = matrix([[1,-1],[2,2]]) D = matrix([[2,0],[0,3]]) A = S*D*S.inverse() show( A.eigenvectors_right() )

Does the result make sense, based on the way that we constructed the matrix ?

Important note: While it is useful to be able to have a computer do these computations, it is also important to be able to do the computations by hand!