Determinants

By Mark Roulo

Last Updated: 23-June-2019


Introduction

This is a discussion of some of the aspects of determinants that seem to be overlooked (or under-emphasized) in linear algebra classes and in pre-calculus classes covering matrixes. It is not a general discussion of determinants.

1) Conceptually, What IS the Determinant?

The determinant of a matrix is a measure of the signed magnitude of the matrix. The only real strangeness is that the magnitude here can be signed, so determinants can be negative. I don't know of a single word that means 'signed magnitude,' so we'll go with magnitude and keep in mind that the value can be zero or negative.

What the magnitude means depends on the 'size' of the matrix, where the 'size' is the number of rows (and 'size' is also the number of columns because for square matrices the number of rows is equal to the number of columns).
The pattern should be obvious.

2) Why Does Determinant Re-use the 'Absolute Value' Symbol?

Because absolute value is another form of magnitude, but for numbers rather than matrixes.

Instead of thinking of absolute value as 'dropping the negative sign,' think of it as 'distance from the origin.'

If you think of distance (or length) as a form of 'magnitude', then the two concepts are pretty close.
NOTE: You should be thinking of absolute value as 'distance from the origin' even if you aren't working with matrixes. The absolute value of complex numbers is NOT computed by 'dropping the negative sign':
|  4 + 3i  | = 5
|  -4 + 3i  | = 5
|  4 - 3i  | = 5
|  -4 - 3i  | = 5

3) Where Does 'ad - bc' Come From?

A commonly used formula for calculating the determinant of a 2-dimensional matrix is:

|   a b c d   |   = ad - bc.

This is easy to compute, but seems fairly arbitrary. Or more than fairly arbitrary. Why does the formula work?

If we consider that a 2-dimensional determinant IS an area, we can draw the following picture, replacing a, b, c, and d with x1, y1, x2, and y2 for clarity:

The determinant:
|   x1 y1 x2 y2   |

is the area of the grey parallelogram in the middle.

Capital letters 'A' - 'F' are the areas of the regions in which the letters reside.

The 'x1', 'x2', 'y1', and 'y2' values on the edges are lengths.

And now for some algebra.

Area of the entire rectangle is: (x1 + x2) · (y1 + y2)
... and expanded is: (x1·y1) + (x1·y2) + (x2·y1) + (x2·y2)
 
subtract piece 'A': - (x2·y1)
subtract piece 'B': - ½(x1·y1)
subtract piece 'C': - ½(x2·y2)
subtract piece 'D': - (x2·y1)
subtract piece 'E': - ½(x1·y1)
subtract piece 'F': - ½(x2·y2)

and ... the area of the Parallelogram: (x1·y2) - (x2·y1)

So the formula IS just the area of the parallelogram ... with all the details of how to get there omitted.

4) How Can Determinants Be Negative?

If the determinant is a magnitude, how can magnitudes be negative?

In some sense, magnitudes shouldn't be negative, but this is why the math folks have another name for this.

One way to think of the sign is that measuring the area while sweeping out the shape counter-clockwise results in a positive measure of magnitude, while sweeping out the shape clockwise results in a negative magnitude. Thus the following two matrixes have determinants with opposite signs.

[ 0 1 1 0 ] and [ 1 0 0 1 ]

For 2×2 matrixes the sweeping works the same way that the angles on a unit circle do: counter-clockwise is positive, and clockwise is negative.

And Some (One!) Fairly Obvious Properties For 2×2 Matrixes

Identity Matrixes Result in Determinants That Are '1'

Identity matrixes are basically vectors of length '1' in each independent dimension.
Vector of length 1: [ 1 ]
Vectors form square with sides of 1: [ 1 0 0 1 ]
Vectors form cube with sides of 1: [ 1 0 0 0 1 0 0 0 1 ]