Quaternions: Why

Before digging into the deep guts of the quaternion, I want to talk a little bit about complex numbers. Complex numbers are really only a step away from quaternions, and people are much less nervous around complex numbers. So it's a pretty good place to start.

A Bit About Complex Numbers:

A complex number has a real and an imaginary component.

z = x +iy

The presence of the i makes these two components linearly independant. Thus we can begin to think of a complex number as a 2D vector. In fact it is convenient to represent a complex number as a (real, imaginary) pair.

z = (x, y)

We know that we can add and subtract complex numbers

z₁ + z₂ = (x₁ + x₂, y₁ + y₂)

z₁ - z₂ = (x₁ - x₂, y₁ - y₂)

and we acheive the same result as adding and subtracting 2D vectors.

Complex numbers can do something that 2D vectors can't. You can multiply them.

z₁z₂ = (x₁x₂ - y₁y₂, x₁y₂ + y₁x₂)

The existence of the multiplication rule promotes the complex numbers from a mere vector space to an algebra. An algebra is a linear space that has a multiplication rule.

If we want the complex numbers to represent a 2D vector space we are missing something, and that's a dot product. In a 2D vector space, we have a dot product that tells us the length of the vectors, (or the length squared of the vectors to be more precise.)

But wait!! We can determine the magnitude squared of a complex number as well:

|z|² = zz^* = (x² + y², 0) = x² + y²

Here the * represents a complex conjugate, which merely flips the sign of the imaginary component. The magnitude squared of a complex number is identical to the result of taking the dot product of a 2D vector with itself. Using this as your starting point, you can show that the vector dot product, defined in terms of a complex algebra is

A•B = (1/2) (AB^* + BA^*)

When endowed with a dot product, the complex numbers truly do contain a full and complete representation of a 2D vector space, with the added bonus of having a well defined way of multiplying the vectors together.

In other words, 2D vectors are not complex numbers, but complex numbers ARE 2D vectors which have the added power of multiplication.

So What Was Hamilton Thinking?

Often, when one is looking up information about quaternions, the story comes up about how W.R. Hamilton is walking along one day, and while crossing a bridge he suddenly comes up with the formula for quaternions. He then promptly vandalizes the bridge and goes on his merry way.

How does someone just come up with quaternions? I will tell you.

Hamilton was aware of the fact that the algebra of complex numbers supply a natural mechanism for multiplying 2D vectors, and he was wondering: How would a person multiply 3D vectors? We add and subtract 3D vectors, and we have a dot product for 3D vectors, but how do you multiply 3D vectors?

Now, some people might be thinking, what about the cross product? Why can't the cross product be the multiplication rule for 3D vectors? The answer is: the cross product definitely holds a clue, but it is not the entire answer. Besides, it wasn't until after the invention of the quaternion that we even had a cross product, so Hamilton didn't know about them.

So, as Hamilton is crossing the bridge it dawns on him how we might multiply 3D vectors together, and he writes the multiplication rules for the 3D basis vectors on the bridge. These multiplication rules are what we are talking about when we say the "definition of a quaternion"

So How Did Hamilton Come Up With the Formula?

Remember how we defined the 2D dot product in terms of the complex multiplication rule? Hamilton similarly decided that if a 3D multiplication rule exists, the result of multiplying a vector with itself should be a real number that is equal to the length squared of the vector. So, however the mutliplication works, it should result in the following formula

VV^† = x² + y² + z²

The meaning of the † here is similar to that of the complex conjugate, but here it flips the signs of all of the basis vectors.

V^† = -xI - yJ - zK

There are nine terms in the product, when the vector is expressed in terms of its 3 components

VV^† = -x²I² - y²J² - z²K² - xy(IJ + JI) - xz(IK + KI) - yz(JK + KJ)

By not combining the IJ and the JI terms, I am stating that they are possibly different.

While crossing the bridge Hamilton realized that he could satisfy his initial postulate about the multiplication of 3D vectors, if the basis vectors satisfied the following multiplicaiton rules.

I² = J² = K² = -1

IJK = -1

In other words, if he applied these rules, the first 3 terms would fall out correctly, and the last 6 terms would vanish.

Hamilton was pretty stoked about this, because this meant that you could add, and subtract 3D vectors, but now you could also multiply them together!

So Where Does the Fourth Component Come From?

Using Hamiltons multiplication rules for the basis vectors, we can define a general multiplication rule for the product of arbitrary 3D vectors.

AB = -A•B + A×B

The first term is a scalar, and the second term is a 3 component vector, four components in all. It was probably this surprising discovery that the multiplication rules summoned the existence of a 4th component that prompted Hamilton to call them quaternions, which literally means "a group of 4."

The existence of the 4th component in a general quaternion does not change the original meaning of the vector portion. The vector portion of a quaternion is truly a 3D vector, in every possible identification. It adds, subtracts, and has a dot product like a standard 3D vector. We can use it in every possible way that we can use any 3D vector. The only magic here is that we now have a multiplication law. This is a good thing, since we can use this multiplication law to make meaningful geometric statements about 3D vectors.

So What Do Quaternions Have To Do With Rotations?

At the very heart of the definition of the quaternion multiplication lies the postulate that it must somehow represent the length of the vector. The fundamental definition of a rotation is that it is a transformation which does not change the length of a vector. Thus the definition of quaternion mutliplication is very intimately connected to the concept of rotation.

Let's build the rotation from the ground up. To begin with, we know that quaternion multiplication from the right is not the same as quaternion multiplication from the left. Thus, the general form of a transformation would look something like this

v' = AvB

Where we have two transformation quaternions, one acting on the right, and one acting on the left.

Since v is a 3D vector, we don't care what the scalar part is. However, the transformation should also not care. This means that the transformation should not change the value of the scalar part. If the scalar part starts at zero, the transformation should leave it zero. Placing this restriction on the general form of the transformation leads to the following condition.

B = A^†

And so our transformation law now looks like this

v' = AvA^†

Finally, we require that the length of v is not changed by the transformation. This can be stated using Hamiltons initial postulate of 3D vector multiplication

v'v'^† = vv^†

AvA^† (AvA^†) = AvA^†Av^†A^†

We see that the only way our condition can be satisfied is if A^†A = 1. In other words A must be normalized. An arbitrary normalized quaternin takes the form

N = cos(α) + n sin(α)

Here n is a normalized 3D vector.

If we use this N to apply the transformation, we will see that we have successfully rotated v around the axis n by an angle of 2α. The reason for the factor of 2, is because there are 2 factors of N acting on v. To take this into account, we generally define rotation quaternions in terms of a half-angle.

r = cos(θ/2) + n sin(θ/2)

Conclusion:

You now know the why behind quaternions. A 3D vector is not a quaternion, but a quaternion IS a 3D vector, with a multiplication law that requires an additional scalar component. Go now, and unleash your newfound power upon the helpless masses.