{GAME23}

GAME23 Sneak Peek

Martin Roelfs, PhD

Lattice Quantum Chromodynamics @ KULAK

GAME23 Sneak Peek

Martin Roelfs

Overview:

Part 1: An Algebra of Geometry
Part 2: Implementing the Algebra of Geometry
Part 3: Closing statements
Part 4: Visit to the ganja.js Coffeeshop

{GAME23}

{Part 1}

An Algebra of Geometry

{Computer Geometry/Physics}

An Algebra for Geometry

Martin Roelfs

// construct lines
line_from_points (p1, p2)
line_from_points_and_dir (p, d)
line_from_plucker (a, b, c, d, e, f)

// construct planes
plane_from_points (p1, p2, p3)
plane_from_point_and_dirs (p, d1, d2)
plane_from_points_and_dir (p1, p2, d)
plane_from_point_and_line (p, l)
plane_from_equation (a, b, c, d)

// Intersections
intersect_line_plane (l, P)
intersect_plane_plane (P1, P2)
intersect_planes (P1, P2, P3)

// Projections
project_point_plane (p, P)
project_line_plane (l, P)
project_point_line (p, l)

// construct transformations
mtx_translate (x, y, z)
mtx_rotate_euler (h, p, b)
mtx_rotate_axis_angle (x, y, z, a)
mtx_look_at (from, too, pole)

// apply transformations
transform_point (M, p)
transform_direction (M, d)
transform_line (M, l)
transform_plane (M, P)

Current state of affairs: linear algebra representation of geometry.

Use multiple bags of real numbers and do all bookkeeping ourselves.
- No inherent geometry in matrices.
Results in a different function for each distinct operation. (Just a subset shown here.)
Tied to a particular number of dimensions

{Computer Geometry}

An Algebra for Geometry

Martin Roelfs

Wouldn't it be better if we had a single bag of numbers, where each element in this bag is an element of geometry?

a \to bab^{-1}

(a \cdot b) b^{-1}

Transform $a$ by $b$:

Project $a$ onto $b$:

(Projective) Geometric Algebra

Teaser: let $a$ and $b$ be any elements of geometry (points, lines, planes, translation, rotations, etc)

- Mathematical operations such as multiplication and addition are always geometrically meaningful

- Not tied to a particular number of dimensions

{GA}

An Algebra for Geometry

Martin Roelfs

The idea of Geometric Algebra in just 30 minutes.

Not just a new way to shuffle coordinates.
Not just rewriting without cross product.
Not just the quotient of the tensor algebra with some ideal.
Not just the extension of vector algebra to multivectors.

+

scalar

vector

bivector

The idea of Geometric Algebra in just 30 mins.

Start from a simple example and Vector Algebra solution
Inspect that method and highlight some issues
Introduce the Geometric Algebra approach
Inspect that method and explore some of the consequences

A simple example ..

$d_i = \big ( \frac 1 f - \frac 1 {d_0} \big )^{-1}\qquad h_i=-\frac {d_i} {d_0} h_0$

Martin Roelfs

An Algebra for Geometry

{GA}

The idea of Geometric Algebra in just 30 mins.

Start from a simple example and Vector Algebra solution
Inspect that method and highlight some issues
Introduce the Geometric Algebra approach
Inspect that method and explore some of the consequences

$d_i = \big ( \frac 1 f - \frac 1 {d_0} \big )^{-1}\qquad h_i=-\frac {d_i} {d_0} h_0$

The paraxial approximation of the thin lens equation

Martin Roelfs

An Algebra for Geometry

{GA}

$d_i = \big ( \frac 1 f - \frac 1 {d_0} \big )^{-1}\qquad h_i=-\frac {d_i} {d_0} h_0$

The paraxial approximation of the thin lens equation

$\vec a = \overline {\vec c - \vec f}$

$\vec b= \vec c - \vec p$

$d_0 = \vec a \cdot \vec b$

Martin Roelfs

An Algebra for Geometry

{GA}

$d_i = \big ( \frac 1 f - \frac 1 {d_0} \big )^{-1}\qquad h_i=-\frac {d_i} {d_0} h_0$

The paraxial approximation of the thin lens equation

$(\overline {\vec c - \vec f}) \, \cdot$

$(\vec c - \vec p)$

$d_0 = $

$h_0 = (\overline{\vec c - \vec g} )\,\cdot\,(\vec c - \vec p) $

Martin Roelfs

An Algebra for Geometry

{GA}

The Geometry of Arrows and its Algebra.

Given the green arrows, all operations are well defined.

Martin Roelfs

An Algebra for Geometry

{GA}

The Geometry of Arrows and its Algebra.

Given the green arrows, all operations are well defined.

Given the green points, no operation makes geometric sense.

Euclid's first postulate : two points determine a single line.

(and nothing else!)

Martin Roelfs

An Algebra for Geometry

{GA}

The Geometry of Arrows and its Algebra.

For any operation $\circ$ between two points to be geometric, its result must be in the 1D line they span!

(that's addition, subtraction, all products ...)

Martin Roelfs

An Algebra for Geometry

{GA}

The Geometry of Arrows and its Algebra.

Lines and Planes are even more problematic, and can not trivially be mapped to arrows or multi-arrows.

Martin Roelfs

An Algebra for Geometry

{GA}

The Geometry of Arrows and its Algebra.

Vector Algebra is a perfect match for Arrow Geometry
Less ideal for points/lines/planes

How can we improve this?

Martin Roelfs

An Algebra for Geometry

{GA}

Homogeneous/Projective Coordinates

Fundamental building block of CG
Projective arrows solve addition of points
Dot and Cross product not in subspace

Projective multiarrows for lines/planes..
Addition/Subtraction become geometric
Typical first GA approach

Addition and Subtraction regain geometric meaning
Results are independent of origin, but visualisation is not!
A dimension of intuition is sacrificed

Martin Roelfs

An Algebra for Geometry

{GA}

For the geometry of points, lines, planes ..

All operations work in the subspace of their arguments
Represent all elements AND transformations within the same algebra

Instead of an algebra of arrows,

we use an algebra build from reflections.

k-reflections

Geometric Gauges

Martin Roelfs

An Algebra for Geometry

{GA}

k-reflections

Geometric Gauges

If $ a= b$, the reflections cancel out: $a^2 = b^2 = \pm 1$.
If $a$ and $b$ intersect, the result is a rotation around their intersection point
If $a$ and $b$ are parallel, the resul is a translation

Martin Roelfs

An Algebra for Geometry

{GA}

k-reflections

Geometric Gauges

Elements = Transformations = k-Reflections

Martin Roelfs

An Algebra for Geometry

{GA}

k-reflections

Geometric Gauges

Each k-reflection (with k>1) can be gauged without changing the result.

Martin Roelfs

An Algebra for Geometry

{GA}

k-reflections

Geometric Gauges

Each k-reflection (with k>1) can be gauged without changing the result.

Martin Roelfs

An Algebra for Geometry

{GA}

Gauges tell us how reflections combine!

Cartan-Dieudonné

Every orthogonal transformation in an n-dimensional embedding space
is composed from at most n reflections

Martin Roelfs

An Algebra for Geometry

{GA}

This flavor of GA is called PGA.

Martin Roelfs

An Algebra for Geometry

{GA}

$ab$ Geometric Product composes reflections

$ab$ $l$ $ba$

Reflection of the point $bc$ in the line $a$: $a (bc) a$

Martin Roelfs

An Algebra for Geometry

{GA}

$ab$ Geometric Product composes reflections

Martin Roelfs

An Algebra for Geometry

Same formula in 3D!
Same formula to calculate the image of a line! (just make $p$ a line!)

Martin Roelfs

An Algebra for Geometry

{GA}

Same formula for a cylindrical lens! (just make the focal and center a line!)

Martin Roelfs

An Algebra for Geometry

{GA}

$d_i = \big ( \frac 1 f - \frac 1 {d_0} \big )^{-1}\qquad h_i=-\frac {d_i} {d_0} h_0$

$h_0 = (\overline{\vec c - \vec g} )\,\cdot\,(\vec c - \vec p) $

$d_0 = (\overline{\vec c - \vec f} )\,\cdot\,(\vec c - \vec p) $

$i = -d_i\overline{(\vec c - \vec f )} - h_i \overline{(\vec c - \vec g)} + \vec c $

no need for :

coordinates
origin
dimensionality
arrows
'solving'

Martin Roelfs

An Algebra for Geometry

{GA}

An Algebra for Geometry

Martin Roelfs

Key take aways

The elements choose the dimensionality of the problem, not the space you represent it in. I.e. you could write down two points in a 17D space, but all their interactions are still 1D!
All binary operators have this property.
Geometric equations are dimension, origin, and signature independent. Pro tip: always try several dimensions, never stick to one.

{Part 2}

Implementing the Algebra of Geometry

All well and good, but how do I actually compute something?

Key observation: performing the same reflection twice is the same as doing nothing.

$$ vvXv^{-1}v^{-1} = v^2 X v^{-2} $$

I.e., $ v^2 = \pm 1 $.

Martin Roelfs

An Algebra for Geometry

{GA}

In a Clifford (Geometric) Algebra, vectors square to scalars. I.e., for $ v \in \mathbb{R}_{p,q,r}, v^2 \in \mathbb{R} $
It is well-known that the products of vectors in a Clifford Algebra are elements of $ \text{Pin}(p,q,r) $.

Therefore our Algebra of Geometry can be represented using normalized vectors in a Clifford Algebra!

What on earth is the Clifford Algebra $ \mathbb{R}_{p,q,r}$?

Martin Roelfs

An Algebra for Geometry

{GA}

i^2 = -1

j^2 = 1

\epsilon^2 = 0

The Clifford Algebra $ \mathbb{R}_{p,q,r}$ has $p$ positive, $q$ negative, and $r$ null basis vectors.

x^2 + 1 = 0 \implies x^2 = -1

Consider the 2D example, $ \mathbb{R}_{2,0,0}$. This has basis vectors $e_1, e_2$, satisfying $e_1^2 = e_2^2 = 1$. Any vector is now of the form:

$$ u = a e_1 + b e_2. $$

Martin Roelfs

An Algebra for Geometry

{GA}

What is the square of $u$?

\begin{aligned} u^2 &= a^2 e_1^2 + b^2 e_2^2 + ab e_1 e_2 + ab e_2 e_1 \\ &= a^2 + b^2 + ab (e_1 e_2 + e_2 e_1) \end{aligned}

But there is nothing special about $e_1$ and $e_2$! In fact, you might have chosen what I called $u$ as one of your basis vectors. Therefore we must have

\begin{aligned} e_1 e_2 + e_2 e_1 = 0 \quad \implies \quad e_1 e_2 = - e_2 e_1 \end{aligned}

such that $ u^2 = a^2 + b^2 $. So orthogonal vectors anti-commute!

Any line is a linear combination $ u = ae_1 + be_2 $.

Martin Roelfs

An Algebra for Geometry

{GA}

We have also found a single point: $ e_{12} := e_1 e_2 $.

So we can now form lines through the origin $ e_{12} := e_1 e_2 $ as $u = a e_1 + b e_2$. What is the square of the point $e_{12}$?

Martin Roelfs

An Algebra for Geometry

{GA}

\begin{aligned} u v &= (a e_1 + be_2) (c e_1 + d e_2) \\ &= (ac e_1^2 + bd e_2^2) + (a d e_1 e_2 + b c e_2 e_1) \\ &= (ac + bd) + (a d - b c) e_{12} \end{aligned}

We recognize the scalar part as the dot product $ u \cdot v = |u||v| \cos\theta $ of the two vectors, whereas the second part is the exterior product $ u \wedge v = |u||v| e_{12} \sin\theta $.

How about the product of two arbitrary lines through the origin?

\begin{aligned} e_{12}^2 = e_1 e_2 e_1 e_2 = - e_1 e_1 e_2 e_2 = - e_1^2 e_2^2 = -1 \end{aligned}

It behaves like the imaginary unit $i$!

\begin{aligned} u v &= |u||v| e^{\theta e_{12}} \end{aligned}

The product of two lines yields Euler's formula!

So we can now make all lines through the point $ e_{12}$, but we only have one point. We were promised origin independence! So where are the other points? To find them, we need another line orthogonal to $e_1$ and $e_2 $.

Martin Roelfs

An Algebra for Geometry

{GA}

Martin Roelfs

An Algebra for Geometry

{GA}

The line at infinity, $e_0$, is orthogonal to $e_1$ and $e_2 $. Besides finding a new line, we also find two new points: $e_{01}, e_{02}$. Because these lie at infinity, they are called directions.

Recall that lines are specified by the linear equation

$$ ax + by + \delta = 0 $$

We can thus represent arbitrary lines in 2D as

$$ u = ae_1 + be_2 + \delta e_0, $$

where $\delta$ is the distance from the origin. Similarly, arbitrary directions (points on the horizon)

$$ p_\infty = y e_{01} + x e_{20} $$

whereas arbitrary points are given by

$$ p = e_{12} + p_\infty = e_{12} + y e_{01} + x e_{20} $$

What is the square of $e_0$? Well, you cannot reflect in the horizon and live to tell about it!

e_0^2 = 0

\begin{pmatrix}x \\ y \\ 0\end{pmatrix}

\begin{pmatrix}x \\ y \\ 1\end{pmatrix}

\mathbb{R}_{2,0,1}

Martin Roelfs

An Algebra for Geometry

{GA}

Question: how would we now rotate around a given point $(x, y)$ by an angle $ \theta $?

p = e_{12} + y e_{01} + x e_{20}

Since $e_0^2 = 0$, all points are created equal:

$$ p^2 = e_{12}^2 = -1$$

Thus all points generate their own local Euler's formula under exponentiation:

$$ R = e^{\theta p} = \cos\theta + p \sin\theta$$

Directions are even easier, because they square to $p_\infty^2 = 0$:

$$ R = e^{a p_\infty} = 1 + a p_\infty$$

Martin Roelfs

An Algebra for Geometry

{GA}

So we can straightforwardly rotate around any point $X_0$ in space by sandwiching any quantity by $R = e^{\theta p /2}$: $ X(\theta) = R X_0 R^{-1} = e^{\theta p /2} X_0 e^{-\theta p /2} $.

Martin Roelfs

An Algebra for Geometry

{GA}

Towards nD

In 2D we reflect in a line, in 3D in a plane, in nD in a hyperplane. In (pseudo-)Euclidean space $ E(p,q) $, hyperplanes satisfy the linear equation

$$ ax + by + cz + ... + \delta = 0 $$

Therefore, hyperplanes can be represented by vectors in $\mathbb{R}_{p,q,1}$, with basis vectors $ \mathbf e_0^2= 0 $ and

\[ \mathbf e_1^2= \ldots = \mathbf e_p^2 = -\mathbf e_{p+1}^2= \ldots = -\mathbf e_{p+q}^2 = 1, \]

as

$$ v = a \mathbf e_1 + b\mathbf e_2 + c\mathbf e_3 + ... + \delta \mathbf e_0 $$

To reflect a hyperplane $w$ in a hyperplane $v$, use conjugation:

$$ w \to v[w] = v w v^{-1} $$

Martin Roelfs

An Algebra for Geometry

{GA}

The PGA for computer graphics: 3DPGA

PGA integrates objects and operators
PGA relates all known computational representations
PGA is real and understandable geometry

PGA empowers the programmer
PGA leads to very frugal, efficient code
PGA is fun !

Martin Roelfs

An Algebra for Geometry

{GA}

Towards nD

Noteworthy spaces

Describes	Group	Name
2D Plane	E(2)	"Planar quaternions"
3D Plane	E(3)	Dual quaternions
Spacetime*	SO(1,3)	Lorentz transformations
Spacetime	Poin(3,1)	Poincare transformations

\mathbb{R}_{2,0,1}

\mathbb{R}_{3,0,1}

\mathbb{R}_{1,3,1}

\mathbb{R}_{1,3,0}

Question: what is the difference between $\mathbb{R}_{1,3,0}$ and $\mathbb{R}_{1,3,1}$?

{Part 3}

Closing remarks

not just a new way to shuffle coordinates.
not just the quotient of the tensor algebra with some ideal.
not just the extension of vector algebra to multivectors.

scalar

vector

bivector

Martin Roelfs

An Algebra for Geometry

{GA}

Duality

Vectors are always $d-1$ dimensional subspaces (hyperplanes). What if we want to make a true point, which remains a point if we change the dimension?

The dual of a $d-k$ dimensional quantity is always a $k$ dimensional quantity. E.g. in 2D, the dual of a line is a point. But in 3D, the dual of a plane, is also a point.

not just a new way to shuffle coordinates.
not just the quotient of the tensor algebra with some ideal.
not just the extension of vector algebra to multivectors.

scalar

vector

bivector

Martin Roelfs

An Algebra for Geometry

{GA}

Duality

not just a new way to shuffle coordinates.
not just the quotient of the tensor algebra with some ideal.
not just the extension of vector algebra to multivectors.

scalar

vector

bivector

Martin Roelfs

An Algebra for Geometry

{GA}

Forces + Torque = Forque

In the body frame :

push at origin = translation

push at infinity = rotation

Force and Torque are both lines.

in any # dimensions.

not just a new way to shuffle coordinates.
not just the quotient of the tensor algebra with some ideal.
not just the extension of vector algebra to multivectors.

scalar

vector

bivector

Martin Roelfs

An Algebra for Geometry

{GA}

Forces + Torque = Forque

The forque line is the dual of the "axis" of the transformation.

2D: push along a line, around a point.
3D: push along a line, around a line.
3+1D: push along a line, around a plane.
etc.

not just a new way to shuffle coordinates.
not just the quotient of the tensor algebra with some ideal.
not just the extension of vector algebra to multivectors.

scalar

vector

bivector

Martin Roelfs

An Algebra for Geometry

{GA}

Closing statements

PGA unifies all linear and angular phenomena:
- Translations and rotations
- Linear and angular momentum
- Force and Torque
PGA allows us to write expressions that work in any number of dimensions
- As it should, because 2D is a subspace of 17D, so your expressions better work in both
- If an expression doesn't work across dimensions and signatures, it doesn't properly capture the geometry of the problem.

Thank you!

For more information, libs, tools and docs ...

https://bivector.net

Martin Roelfs

An Algebra for Geometry

{GA}

On to the ganja.js Coffeeshop!

{Part 2}

Beyond Cartan-Dieudonné: The Discovery of Invariant Geometry

What happens to points in the plane when we perform an increasing number of reflections?

Martin Roelfs

The Discovery of Invariant Geometry

{GA}

What happens to points in 3D space when we perform an increasing number of reflections?

Martin Roelfs

The Discovery of Invariant Geometry

One mans linear momentum is another mans angular momentum

Martin Roelfs

The Geometry of Physics

{GA}

Wigner rotation

Martin Roelfs

The Geometry of Physics

{GA}

Alice has velocity $ u_A $, while Bob has velocity $ u_B $. In order for Alice to see the world from Bob's perspective, she could simply reflect over the bisector

$$ \overline{u_A + u_B} = \frac{u_A + u_B}{\sqrt{2 + 2 u_A \cdot u_B}} $$

However, this would leave her PT inverted, which may or may not be fatal, so it's best to first perform an extra reflection in place just to be sure:

$$ \Lambda = (\overline{u_A + u_B}) u_A^{-1} $$

This is the boost to transform from Alice to Bob:

$$ \Lambda u_A \widetilde{\Lambda} = u_B$$

{An Algebra for Geometry}

Part 1: Theory and motivation

Introduction to Geometric Algebra in just 15 mins.

not just a new way to shuffle coordinates.
not just the quotient of the tensor algebra with some ideal.
not just the extension of vector algebra to multivectors.

+

scalar

vector

bivector

Start from purely geometric reasoning.
Find if there is a known mathematical structure onto which this maps.

Martin Roelfs

An Algebra for Geometry

{GA}

The atoms of Geometry turn out to be reflections

Martin Roelfs

An Algebra for Geometry

{GA}

Thank you!

For more information, libs, tools and docs ...

https://bivector.net

Martin Roelfs

An Algebra for Geometry

{GA}

{An Algebra for Geometry}

Part 2: Refresher and Hands on

Hands-on session on Geometric algebra

not just a new way to shuffle coordinates.
not just the quotient of the tensor algebra with some ideal.
not just the extension of vector algebra to multivectors.

+

scalar

vector

bivector

In the previous session we learned that the atoms of geometry are reflections
Reflections and elements of geometry are in a one-to-one correspondence. E.g. plane(-reflections), line(-reflections), point(-reflections).

Martin Roelfs

An Algebra for Geometry

{GA}

We will see how to construct planes, lines and points in 3D
Examples

Answer to some questions

not just a new way to shuffle coordinates.
not just the quotient of the tensor algebra with some ideal.
not just the extension of vector algebra to multivectors.

+

scalar

vector

bivector

Theofanis: why it is worth knowing PGA if your (CAD) software already has functions for everything.

Martin Roelfs

An Algebra for Geometry

{GA}

// construct lines
line_from_points (p1, p2)
line_from_points_and_dir (p, d)
line_from_plucker (a, b, c, d, e, f)

// construct planes
plane_from_points (p1, p2, p3)
plane_from_point_and_dirs (p, d1, d2)
plane_from_points_and_dir (p1, p2, d)
plane_from_point_and_line (p, l)
plane_from_equation (a, b, c, d)

// Intersections
intersect_line_plane (l, P)
intersect_plane_plane (P1, P2)
intersect_planes (P1, P2, P3)

// Projections
project_point_plane (p, P)
project_line_plane (l, P)
project_point_line (p, l)

// construct transformations
mtx_translate (x, y, z)
mtx_rotate_euler (h, p, b)
mtx_rotate_axis_angle (x, y, z, a)
mtx_look_at (from, too, pole)

// apply transformations
transform_point (M, p)
transform_direction (M, d)
transform_line (M, l)
transform_plane (M, P)

Answer: if you call a couple of such functions in a sequence, there is no way to see if this can be simplified. If all of these operations are algebraic expressions on the other hand, you can simplify them.

Alexander: can we do extrusions? Example will follow later.
Peter: how does this compare to matrices? Answer: I'll have a slide on this in a moment.

not just a new way to shuffle coordinates.
not just the quotient of the tensor algebra with some ideal.
not just the extension of vector algebra to multivectors.

scalar

vector

bivector

Martin Roelfs

An Algebra for Geometry

{GA}

In 2D we reflect in a line, in 3D in a plane, in nD in a hyperplane. In (pseudo-)Euclidean space $ E(p,q) $, hyperplanes satisfy the linear equation

$$ ax + by + cz + ... + \delta = 0 $$

Therefore, hyperplanes can be represented by vectors in $\mathbb{R}_{p,q,1}$, with basis vectors $ \mathbf e_0^2= 0 $ and

\[ \mathbf e_1^2= \ldots = \mathbf e_p^2 = -\mathbf e_{p+1}^2= \ldots = -\mathbf e_{p+q}^2 = 1, \]

as

$$ v = a \mathbf e_1 + b\mathbf e_2 + c\mathbf e_3 + ... + \delta \mathbf e_0 $$

To reflect a hyperplane $w$ in a hyperplane $v$, use conjugation:

$$ w \to v[w] = - v w v^{-1} $$

Reflections are involutory, and thus demanding

$$ v[v[w]] = w $$

leads to $ v^2 = v^{-2} = \pm 1 $.

Importantly, $ v[v] = -v $.

not just a new way to shuffle coordinates.
not just the quotient of the tensor algebra with some ideal.
not just the extension of vector algebra to multivectors.

scalar

vector

bivector

Martin Roelfs

An Algebra for Geometry

{GA}

u_1 \wedge u_2 \wedge \cdots \wedge u_k

In $d$ dimensions, we start from reflections in $ d-1 $ dimensional hyperplanes. $k$ of these can then be intersected to form $ d - k $ dimensional elements of geometry.

not just a new way to shuffle coordinates.
not just the quotient of the tensor algebra with some ideal.
not just the extension of vector algebra to multivectors.

scalar

vector

bivector

Martin Roelfs

An Algebra for Geometry

{GA}

The composition of two reflections leads to a handedness preserving isometry.

We call such transformations

Bireflections

\begin{aligned} u[v[A]] &= uvA v^{-1}u^{-1} \\ &= (uv) A (uv)^{-1} \end{aligned}

Bireflections $ R = uv $ preserve handedness, and are continuous transformations.

Why two-sided transformations? If I ask you to write upside down, what is the easiest way to do it?

Comparison to matrices

not just a new way to shuffle coordinates.
not just the quotient of the tensor algebra with some ideal.
not just the extension of vector algebra to multivectors.

+

scalar

vector

bivector

Martin Roelfs

An Algebra for Geometry

{GA}

\begin{pmatrix} \vec{p}_\infty' \\ p_o' \end{pmatrix} = \begin{pmatrix} R & t \\ 0 & 1 \end{pmatrix} \begin{pmatrix} \vec{p}_\infty \\ p_o \end{pmatrix}, % \mqty[\textcolor{Fuchsia}{\mathbf R} & \textcolor{Fuchsia}{\mathbf{t}} \\ \textcolor{Fuchsia}{\mathbf{0}} & \textcolor{Fuchsia}{1}] \mqty[\cev{t}_\infty \\ \cev{t}_o].

In order to transform a point by a rotation and a translation in linear algebra, e.g. open3d.geometry.PointCloud.transform, we use the covariant transformation

where $ \vec{p}_\infty $ is a 3-vector indicating a point on the horizon, $p_o $ is the origin, $R$ is a 3x3 rotation matrix, and $t$ is a 3-vector encoding the translation.

This 4x4 matrix uses 16 floats, and can only transform points. For planes we have to use the 4x4 contravariant rep., and for lines the 6x6 adjoint rep. By contrast, the PGA equivalent rotor

$$ R = a + b\mathbf{e}_{23} + c\mathbf{e}_{31} + d\mathbf{e}_{12} + e\mathbf{e}_{01} + f\mathbf{e}_{02} + g\mathbf{e}_{03} + h\mathbf{e}_{0123} $$

transforms points, lines and planes identically using only 8 floats.

not just a new way to shuffle coordinates.
not just the quotient of the tensor algebra with some ideal.
not just the extension of vector algebra to multivectors.

+

scalar

vector

bivector

Martin Roelfs

An Algebra for Geometry

{GA}

What about performance ?

4x4 matrix multiplication : 64 multiplications, 48 additions, 16 floats

PGA motor multiplication : 48 multiplications, 40 additions, 8 floats

with a similar picture for the other elements :

not just a new way to shuffle coordinates.
not just the quotient of the tensor algebra with some ideal.
not just the extension of vector algebra to multivectors.

scalar

vector

bivector

Martin Roelfs

An Algebra for Geometry

{GA}

As

Martin Roelfs

An Algebra for Geometry

{GAME23}

{GAME23}

{Part 1}

{Computer Geometry/Physics}

{Computer Geometry}

{GA}

{GA}

{GA}

{GA}

{GA}

{GA}

{GA}

{GA}

{GA}

{GA}

{GA}

{GA}

{GA}

{GA}

{GA}

{GA}

{GA}

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{GA}

{GA}

{GA}

{GA}

{GA}

{GA}

{GA}

{GA}

{GA}

{GA}

{GA}

{GA}

{GA}

{GA}

{GA}

{GA}

{Part 2}

{GA}

{GA}

{GA}

{GA}

{GA}

{GA}

{GA}

{GA}

{GA}

{GA}

Towards nD

{GA}

The PGA for computer graphics: 3DPGA

{GA}

Towards nD

{Part 3}

{GA}

{GA}

{GA}

{GA}

{GA}

Closing statements

{GA}

{Part 4}

{Part 2}

{GA}

{GA}

{GA}

{Part 3}

{GA}

{GA}

{GA}

{GA}

{An Algebra for Geometry}

{GA}

{GA}

{GA}

{An Algebra for Geometry}