#vector #algebra #geometry #clifford #grassmann


A robust and generalized library for Geometric Algebra in Rust

1 unstable release

0.1.0 Jan 24, 2022

#541 in Math



A robust and generalized library for Geometric Algebra in Rust.

What is Geometric Algebra?

Geometric algebra is an extension of linear algebra where vectors are extended with a multiplication operation called the geometric product, which produces new kinds of operations and vector-like objects that are useful in simplifying and generalizing a number of tricky systems in math and physics.

For instance:

  • In physics, using "bivectors" geometric algebra provides a consistent and clean way of representing angular velocities in any dimension, which normally isn't possible in the standard system of angles and vectors.
  • Geometric algebra unifies the rotational actions of Complex numbers and Quaternions into a single object called a Rotor that works in all dimensions.
  • Using "unit blades", geometric algebra gives a compact and efficient way to represent vector and affine spaces that's easier to work with than sets of basis vectors.

For more information and an in depth explanation, I highly recommend Geometric Algebra for Computer Science by Leo Durst, Daniel Frontijne, and Stephann Mann. A lot of inspiration for this library comes from that book, and the concepts are explained in a way that's easy to follow and visual without falling too deep into overly abstract math.


This crate is split up into a number of modules each representing a different interpretation of the underlying algebra:

  • algebra contains the most raw form of Geometric algebra defined purely algebraically. This is primarily used as the core all the other systems are built upon, but it is useful as well for various physical quantities (like position and angular velocity) and abstract mathematical objects
  • subspace interprets the structs in algebra as weighted vector spaces and orthogonal transformation (like rotations and reflections) in ℝn
  • homogenous (planned) interprets the structs in algebra as affine spaces and their operations in ℝn-1 using homogeneous coordinates

Additionally, the module wedged::base contains the core components common to all subsystems.

Nomenclature and Conventions

Naming in geometric algebra can vary slightly across authors, so the particular conventions have been chosen:

  • in wedged, a SimpleBlade refers to an object constructed from the wedge product of vectors whereas a Blade refers to any object of a particular grade
  • a Rotor is used exclusively for objects with unit norm that encode rotations whereas Even is used for a general object from the even subalgebra
  • Multivectors refer to a general element in the algebra
  • The operators */Mul, ^/BitXor, and %/Rem are used for the geometric, wedge, and generalized dot products respectively

Additionally, most structs in this crate support indexing where each component is ordered according to a particular basis convention chosen for this crate. See the docs on BasisBlade For more information.

Generic programming

In order to generalize code for different scalar types and dimensions, wedged makes heavy use of generics. The system is based upon the system in nalgebra, and, to increase interoperability, reuses a lot of its components.

The system is designed such that a number of different levels of abstraction are possible:

No Generics

use wedged::algebra::*;

let v1 = Vec3::new(1.0, 0.0, 0.0);
let v2 = Vec3::new(0.0, 1.0, 0.0);
let plane = v1 ^ v2;
let cross = plane.dual();

assert_eq!(plane, BiVec3::new(0.0,0.0,1.0));
assert_eq!(cross, Vec3::new(0.0,0.0,1.0));

When not using generics, things act much like you would expect from a vector library.

Generic Scalar

use wedged::algebra::*;
use wedged::base::ops::*;

fn midpoint<T:Clone+ClosedAdd+ClosedDiv+One>(v1:Vec3<T>, v2:Vec3<T>) -> Vec3<T> {
  let two = T::one() + T::one();
  (v1 + v2) / two

fn cross<T:RefRing>(v1:Vec3<T>, v2:Vec3<T>) -> Vec3<T> {
  (v1 ^ v2).dual()

let v1 = Vec3::new(1.0, 0.0, 0.0);
let v2 = Vec3::new(0.0, 1.0, 0.0);
assert_eq!(cross(v1,v2), Vec3::new(0.0,0.0,1.0));
assert_eq!(midpoint(v1,v2), Vec3::new(0.5,0.5,0.0));

let v1 = Vec3::new(1.0f32, 0.0, 0.0);
let v2 = Vec3::new(0.0f32, 1.0, 0.0);
assert_eq!(cross(v1,v2), Vec3::new(0.0f32,0.0,1.0));
assert_eq!(midpoint(v1,v2), Vec3::new(0.5f32,0.5,0.0));

For generic scalars, wedged structs implement traits and functions based on what their scalars implement. For instance:

  • Structural traits like Index, AsRef<[T]>, and Borrow<[T]> are always implemented
  • Basic traits like Clone, fmt traits, PartialEq, Eq, Hash, and Default are all implemented if the scalar implements them
  • Copy is implemented if the scalar is Copy the struct isn't a Dynamic dimension
  • Additive operations like Add, Sub, and Neg as well as scalar operations like Mul and Div. are implemented if the scalar has them. The assign variants and operations between references are implemented as well if the scalars implement them.
  • Traits for the geometric (Mul), wedge (BitXor), and dot (Rem) products are implemented if the scalar has addition, subtraction, negation, zero and multiplication between references
  • anything involving a norm, constructing a rotation, etc, requires nalgebra::real_field
  • Div usually has the same requirements as Mul, but is only implemented on a few specific structs (like Rotor and Versor)

In order to make managing these requirements easier, a number of trait aliases are included in wedged::base::ops that combine common groupings of operations together

Generic Dimension

use wedged::base::*;
use wedged::algebra::*;

fn point_velocity<T,N:Dim>(p:VecN<T,N>, angular_vel: BiVecN<T,N>) -> VecN<T,N> where
  p % angular_vel

let p = Vec2::new(2.0,0.0);
let av = BiVec2::new(3.0);
assert_eq!(point_velocity(p, av), Vec2::new(0.0,6.0));

let p = Vec3::new(0.0,2.0,0.0);
let av = BiVec3::new(3.0,0.0,0.0);
assert_eq!(point_velocity(p, av), Vec3::new(0.0,0.0,6.0));

For a generic dimension, for every wedged struct used in the function or trait, the scalar must have the corresponding Alloc* trait bound for that struct. These are all included in wedged::base::alloc, and there is one for each subset of the geometric algebra.

For example, for a scalar T and dimension N, using a VecN requires T:AllocBlade<N,U1> bound, using an Even needs a T:AllocEven<N> bound, etc.

Finally, even if a non-generic scalar is used, these are still required inside a where clause on the function or trait.

Generic Dimension and Grade

use wedged::base::*;
use wedged::algebra::*;

fn project<T,N:Dim,G:Dim>(v:VecN<T,N>, b:Blade<T,N,G>) -> VecN<T,N> where
  U1: DimSymSub<G>,
  DimSymDiff<U1,G>: DimSymSub<G,Output=U1>,
  T:AllocBlade<N,U1> + AllocBlade<N,G> + AllocBlade<N,DimSymDiff<U1,G>> + RefDivRing
  let l = b.norm_sqrd();
  (v % &b) % b.reverse()/l

let v = Vec3::new(1.0, 2.0, 0.0);
let line = Vec3::new(0.0, 1.0, 0.0);
let plane = BiVec3::new(0.0, 1.0, 0.0);

assert_eq!(project(v,line), Vec3::new(0.0, 2.0, 0.0));
assert_eq!(project(v,plane), Vec3::new(1.0, 0.0, 0.0));

Using a generic grade follows nearly the same rules as for generic dimensions but with an added generic for the grade. However, with grade altering operations like the wedge or dot products are used, additional bounds are required to allow the compiler to add or subtract the grades of the two blades.

For instance:

  • The wedge ^ product requires G1: DimAdd<G2>
  • The dot % product requires G1: DimSymSub<G2>


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