{# LANGUAGE OverloadedLists #}
module AllHailGeometricAlgebra where
import Control.Monad
import Data.List
import Data.Map (Map)
import qualified Data.Map as M
All Hail Geometric Algebra!
Rotate a cube by 90 degrees around the \(x\)axis, then rotate it by 90 degrees around the \(z\)axis. This is equivalent to a single rotation. What is the axis and angle of this single rotation?
If we hold a physical cube in our hands, we can turn it a couple of times and intuitively find the answer. Is there a similarly straightforward mathematical method that rigorously solves this problem?
What if we generalize? That is, given any two rotations that fix the origin, what is the single rotation that is equivalent to their composition? Can we solve this just as easily?
Yes! With geometric algebra, we can solve the first problem comfortably with pen and paper, and apply the same method to its generalization.
We’ll take a whirlwind tour of A Survey of Geometric Algebra and Geometric Calculus by Alan Macdonald. We’ll also assume our constructions are welldefined; for proofs, refer to An elementary construction of the geometric algebra by the same author. See also:

A paper comparing geometric algebra and vector algebra for ray tracing by Daniel Fontijne and Leo Dorst.
We’ll explain some ideas in code, for which we need a few declarations. (The source of this page is literate Haskell.)
A number is worth 1000 pictures
I was spellbound by Cartesian coordinates as a child. Humble numbers taming the wild world of geometry. Instead of dubiously hoping a handdrawn picture represents the truth, we can solve geometry problems rigorously by mechanically applying the laws of algebra.
However, in practice, coordinates are unwieldy. For example, try finding the area of a triangle by determining its side lengths with Pythagoreas' Theorem, then plugging them into Heron’s formula. Or try finding the point of intersection between a line and a plane by solving simultaneous equations.
Cartesian coordinates are the assembly language of geometry. Unimportant details clutter our reasoning. Highlevel ideas are difficult to express.
Nonetheless, coordinates suffice for solving our first problem in a rough manner: we take a cube centered at the origin and figure out where its corners go. We then determine the axis by finding which corners are fixed, and determine the angle by counting rotations until the cube returns to the original position. Of course, this method fails for arbitrary axes and angles.
I could not have invented vector algebra!
Years later, I learned about vectors, which simplified calculations. For example, the area of a triangle is basically a cross product. Though grateful for the labour saved, the mysterious ad hoc nature of these new tools bothered me. How did they figure it all out?
The dot product almost looks like it could be discovered algebraically: we might consider applying the multiplication from the direct sum of rings, that is, zipWith (*) v w. We may notice that the squared norm of a vector v is sum (zipWith (*) v v), which suggests we may eventually realize sum (zipWith (*) v w) has geometric significance.
However, the cross product is baffling. Without a superstructure such as quaternions (which themselves took some effort to discover), the only plausible route to discovering it is to derive the algebra from the geometry. Lagrange did this in 1773.
Even if we manage to derive them somehow, these products are pale shadows of the products of abstract algebra. The dot product is a scalar, a type different to the inputs. Calling it a product feels like an abuse of terminology. The cross product only works in precisely three dimensions, and although it satisfies some identities, it is far from elementary, For starters, it’s nonassociative. Is a product with nice properties too much to ask?
And how about Plücker coordinates? They seem underappreciated even by those wellacquainted with cross products, likely because they are abstruser still.
Worst of all, despite all the above machinery, lowlevel details remain. We would like to write down a handful of symbols describing geometric concepts, then manipulate them with the laws of algebra to perform amazing feats. Vector algebra only partially succeeds.
If we’re skilled with matrices, we can solve our rotation problem by following along the proof of Euler’s rotation theorem. We multiply the matrices for the two given rotations to produce a single matrix, then find an eigenvector to determine the axis of rotation. As for the angle, we could study the effect of the matrix on a vector perpendicular to the axis.
This is disproportionately tedious. Why must we work so hard to handle a simple idea like rotation?
In contrast, geometric algebra gives a clean and simple mathematical framework for rotations, built from a clean and simple vector product. We really can scribble down a few symbols and apply the laws of algebra to do geometry.
Additionally, the weirdest parts of vector algebra, including the cross product and Plücker coordinates, arise naturally in geometric algebra.
Complex numbers simplify
Thanks to polar coordinates, complex numbers excel at describing 2D rotations. See also phasors.
Complex numbers should be more widely used, even for discrete problems. For example, when navigating a 2D maze, we might need to rotate our orientation left or right by 90 degrees. If we index the array with Gaussian integers, 90degree rotations are simply multiplications by \(\pm i\).
To go beyond 2D, we can augment complex numbers so they become quaternions, which excel at describing 3D rotations.
A contentious article advocates vectors over quaternions. While the vigorous prose is admirable, the argument is weak. The claim that quaternions are “very complicated” is false, but more risible is the howler: “But why has nobody looked beyond quaternions for a simpler solution until now?”. In fact, a simpler solution was found long ago: geometric algebra.
The real problems with quaternions are that translations and rotations mix poorly, and that they limit us to 3D.
Geometric algebra is a common framework for vector algebra and quaternions. Despite its power, geometric algebra is simple, and rotations in any dimension closely resemble the elegant descriptions of 2D rotations with complex numbers and 3D rotations with quaternions.
Thus the conclusion of the article is right for the wrong reasons. We should skip learning quaternions, but not because vectors are better. Rather, it’s because we should study the more powerful geometric algebra, which teaches us both quaternions and vector algebra along the way.
Geometric algebra for (almost) free
We can trivially define a product with nice properties on any set: just take the free monoid, or as programmers call it, string concatenation. (Here, each character represents an element of the set and the empty string is the identity element.)
Similarly, we can define a vector product with nice properties thanks to free algebras. Again, we concatenate strings: given vectors \( \newcommand{\n}{\mathbf{n}} \newcommand{\u}{\mathbf{u}} \newcommand{\v}{\mathbf{v}} \newcommand{\w}{\mathbf{w}} \newcommand{\i}{\mathbf{i}} \newcommand{\e}{\mathbf{e}} \newcommand{\f}{\mathbf{f}} \newcommand{\vv}[1]{\mathbf{#1}} \u, \v, \w\), we can form products such as \(\vv{www}\), \(\vv{vuwvwuuvw}\), and so on. Then we mix in scalars, and add and multiply them together to get polynomiallike expressions, which we call multivectors. For example:
In Haskell:
type Multivector = Map String Double
freeMul :: Multivector > Multivector > Multivector
freeMul x y = M.filter (/= 0) $
M.fromListWith (+) $ f <$> M.assocs x <*> M.assocs y where
f (s, m) (t, n) = (s ++ t, m * n)
prop_exampleFreeMul = freeMul [("vuvw", 2), ("", 1)] [("v", 3), ("", 2)]
== [("vuvwv", 6), ("vuvw", 4), ("v", 3), ("", 2)]
This product is nice by definition. There is an identity element. It’s associative. It’s distributive over vector addition. What a shame it’s also useless: how do we geometrically interpret \(\vv{uvwuuv}\)?
There’s no way. But by constraining our free algebra with one simple relation, we obtain the vaunted geometric algebra. We define the geometric product by subjecting the free product to the following constraint: for any vector \(\v\),
Incidentally, we can generalize this definition by replacing the dot product with any quadratic form, and generalize further to Clifford algebras by considering any vector space over any field. For now, we focus on the standard dot product on \(\mathbb{R}^n\), where the resulting geometric algebra is denoted \(\mathbb{G}^n\).
We could have instead written \(\v^2 = \v^2\), but this hides our need for certain properties of the standard inner product. Specifically, we need the polarization identity:
From our constraint, this is:
By elementary algebra, this implies:
Hence when \(\u\) and \(\v\) are orthogonal, we have \(\u\v = \v\u\).
As its name suggests, the geometric product is useful for geometry, and also possesses nice algebraic properties. Even division is possible sometimes: the inverse of a nonzero vector \(\v\) is \(\v / \v^2\).
[By the way, this section’s title is unoriginal. Also, while I could not have invented geometric algebra either, at least it’s simpler than vector algebra.]
The canonical basis
To recap:

We can replace \(\v\v\) with \(\v^2\).

We can replace \(\u\v\) with \(\v\u\) when \(\v\) and \(\u\) are orthogonal.
Thus if \(\e_1, ..., \e_n\) is an orthonormal basis for \(\mathbb{R}^n\), then every multivector of \(\mathbb{G}^n\) can be written as a linear combination of products of the form:
where \(i_1,...,i_k\) is a strictly increasing subsequence of \([1..n]\). These products are a canonical basis for \(\mathbb{G}^n\), which implies the basis for an \(n\)dimensional vector space yields a canonical basis of size \(2^n\) for its geometric algebra.
Here’s a function showing the details. It computes the geometric product in canonical form, assuming each character that appears in the input represents an orthonormal basis vector of the space. For clarity, we employ the inefficient gnome sort:
 Computes geometric product of multivectors written in terms of some
 orthonormal basis. Returns result in canonical form.
gMul :: Multivector > Multivector > Multivector
gMul x y = M.filter (/= 0) $
M.fromListWith (+) $ basisMul <$> M.assocs x <*> M.assocs y
basisMul (s, m) (t, n) = gnome [] (s ++ t) (m * n) where
gnome pre (a:b:rest) c
 Vectors are in order: move on.
 a < b = gnome (pre ++ [a]) (b:rest) c
 Same vector: remove both copies since magnitude = 1.
 a == b = back pre rest c
 Wrong order: flip and back up one step. Since the vectors are
 orthogonal, we flip the sign of the geometric product.
 a > b = back pre (b:a:rest) (c)
where
back [] rest c = gnome [] rest c
back pre rest c = gnome (init pre) (last pre:rest) c
gnome pre rest c = (pre ++ rest, c)
canon = gMul [("", 1)]
prop_exampleOrthonormal = canon [("uvwuuv", 1)] == [("uw", 1)]
 Returns canonical basis given orthonormal basis.
canonicalBasis :: String > [String]
canonicalBasis = subsequences
If the \(k\) is the same for every term when an multivector is written canonically, then we say the multivector is a \(k\)vector. A 0vector is also called a scalar, a 1vector a vector, a 2vector a bivector, and a 3vector a trivector:
 Assumes input is in canonical form.
name :: Multivector > String
name x
 null x = "zero multivector"
 all ((== k) . length) ss = show k ++ "vector" ++ t
 otherwise = "mixed multivector"
where
s:ss = M.keys x
k = length s
t  null (aka k) = ""
 otherwise = " or " ++ aka k
aka 0 = "scalar"
aka 1 = "vector"
aka 2 = "bivector"
aka 3 = "trivector"
aka _ = ""
prop_exampleNames = and
([ name [] == "zero multivector"
, name [("", 42)] == "0vector or scalar"
, name [("x", 5), ("y", 98)] == "1vector or vector"
, name [("ab", 2), ("bc", 3)] == "2vector or bivector"
, name [("abc", 4)] == "3vector or trivector"
, name [("abcde", 0.01)] == "5vector"
, name [("", 2), ("bc", 3)] == "mixed multivector"
] :: [Bool])
(We omit the proof the above is welldefined, that is, a \(k\)vector is a \(k\)vector no matter which orthonormal basis is chosen.)
Lastly, \(n\)vectors are also called pseudoscalars. We write \(\vv{I}\) for the pseudoscalar \(\e_1 ... \e_n\), which is unique up to sign for any choice of basis. (This is implied by the omitted proof.) Then \(\vv{I}^{1} = \e_n ... \e_1 = (1)^{n1}\vv{I}\).
The fundamental identity
Let’s try a couple of 2D examples. Let \(\e, \f\) be an orthonormal basis of a plane.
prop_2Dsquare = join gMul [("e", 3), ("f", 4)] == [("", 25)]
prop_2Dmul = gMul
[("e", 3), ("f", 4)] [("e", 5), ("f", 6)] == [("", 39), ("ef", 2)]
prop_2DmulOrth = gMul
[("e", 3), ("f", 4)] [("e", 4), ("f", 3)] == [("ef", 25)]
We see the geometric product of two vectors is a scalar and a bivector. Furthermore, appearances of \(0\) and \(25 = 5^2 = 3^2 + 4^2\) hints at a deeper truth. Expanding \((a \e + b \f)(c \e + d \f)\) proves the fundamental identity: for any vectors \(\u\) and \(\v\) in our plane,
where \(\u\wedge\v\) is the outer product or wedge product, the bivector component of the geometric product of two vectors, and satisfies:
where \(\theta\) is the angle between \(\v\) and \(\w\), and \(\i\) is the bivector \(\e \f\).
We interpret \(\i\) to mean the plane generated by \(\e\) and \(\f\). With vector algebra, we represent planes with normal vectors, which only makes sense in exactly three dimensions. With geometric algebra, we represent a plane by multiplying two orthonormal vectors that generate them; this works in any dimension.
We find \(\i^2 = 1\), which explains the choice of notation. In fact, for any given plane we can construct a complex number system in this manner, that is, multivectors of the form \(a + b\i\). Such numbers are called geometric algebra complex numbers.
We can also write:
which resembles a standard complex number in polar form.
Suppose \(\u = \v\). Then left multiplying by \(u\) gives:
Thus the geometric algebra interpretation of \(e^{\i\theta}\) is a rotation by \(\theta\) in the plane \(\i\), rather than a particular point on a unit circle measured from some predefined zero bearing. This contrast is a rotational analogue of the difference between vectors and Cartesian points.
Rotations, in any dimension
We noted complex numbers excel at describing rotations in two dimensions, and quaternions in three. Geometric algebra complex numbers excel at describing rotations in any dimension.
Let \(\i\) be the product of two orthonormal vectors representing the plane in which we wish to rotate. Let \(\theta\) be the angle about the origin we wish to rotate. Let \(\u\) be a vector.
Decompose \(\u\) with respect to \(\i\):
That is, we find vectors \(\u_{\perp} \cdot \i = 0\) and \(\u_{\parallel} \wedge \i = 0\) satisfying the above summation. These are unique and readily computed, but for this section we only need their existence.
Rotation only affects the second term, so \(\u\) rotates to:
where we have used \(\u_\perp \i = \i \u_\perp\) and \(\u_\parallel \i =  \i \u_\parallel\), identities that can be verified by setting \(\i = \e\f\) for orthonormal vectors \(\e,\f\) and a touch of algebra.
In 3D, choose a unit normal \(\n\) to \(\i\) so that \(\e\f\n =\vv{I}\). Then we can phrase a rotation of a vector \(\u\) about the axis \(\n\) by the angle \(\theta\) as:
This formula implies Euler’s rotation theorem, as composing two rotations results in an expression of the same form, but our argument has gaps since we skipped some proofs.
The multivector \(e^{\n\vv{I}\theta/2}\) is sometimes called a rotor.
Problem solved
We now solve our opening problem with a minimum of paperwork. Let \(\newcommand{\x}{\vv{x}} \newcommand{\y}{\vv{y}} \newcommand{\z}{\vv{z}} \x, \y, \z\) be an orthonormal basis of \(\mathbb{R}^3\), so \(\vv{I} = \x\y\z\). We focus on the right half of the rotation formula. For a rotation of 90 degrees around the \(x\)axis followed by a rotation of 90degrees around the \(z\)axis, it reads:
for some constant \(c \gt 0\). Therefore the combined rotation is 120 degrees around the axis \(\x + \y + \z\), or in Cartesian coordinates, the axis through the origin and (1, 1, 1).
We can easily write code to compute the axis and angle of the composition of any two 3D rotations with axes through the origin, and use it to check our work:
rotr :: [Double] > Double > Multivector
rotr axis theta = canon [("", c), ("yz", nx*s), ("zx", ny*s), ("xy", nz*s)]
where
c = cos (theta / 2)
s = sin (theta / 2)
[nx, ny, nz] = map (/ sqrt(sum $ map (^2) axis)) axis
m!s = M.findWithDefault 0 s m
axisAngle :: Multivector > ([Double], Double)
axisAngle m = ([m!"yz", (m!"xz"), m!"xy"], acos (m!"") * 2)
prop_rotationExample =
(theta * 180 / pi) ~== 120 && all (~== 1) (map (/ head axis) axis)
where
a ~== b = abs (a  b) < 10**(6)
x90 = rotr [1, 0, 0] $ pi/2
z90 = rotr [0, 0, 1] $ pi/2
(axis, theta) = axisAngle $ gMul x90 z90
We can animate these rotations on this very webpage using these geometric algebra routines by generating JavaScript from Haskell code that draws cubes.
We’ve barely scratched the surface. With the geometric algebra homogeneous model of \(\mathbb{R}^n\), we get:

4x4 matrices for manipulating points in 3D, just like with vector algebra: rotations, translations, perspective projection, and so on.

Simple formulas for subspaces (lines, planes, and beyond). for example, a line is defined by two points \(p, q\), so its equation is \(p \wedge q\); a plane is defined by three points \(p, q, r\) , so its equation is \(p \wedge q \wedge r\).

Simple formulas for transforming subspaces, aka outermorphisms: \(f(p \wedge q) = f(p) \wedge f(q)\).

Simple formulas for finding the intersection of subspaces: \(a^* \cdot b\).