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Geometric Algebra




Vitor Fernando Pamplona
Cross Product in nD?
• Why not?
  – V1 = (1.0, 2.0, 3.0, 4.0)
  – V2 = (-2.0, -3.0, -4.0, -5.0)


                               V1×V2=? ?


                  I       J      K       Q
                1,00    2,00   3,00    4,00    =   ???
               -2,00   -3,00   -4,00   -5,00
A language for geometry

               Geometric      Algebraic
               Concepts       Language



• Magnitude - size
                              3D       4,20
• Direction -orientation
• Sense - negative/positive
                                      35º
• Grade - dimension             CCW
                                            x
What we need?
• n-dimensional geometric language

• Represent the object grade

• Operate across different dimensions

• Geometrically intuitive

• Without limits

• Coordinate free

• Efficient

• Unify other algebras
Product of Vectors ?

                       vv = ?

                   v   vv = 3 e1  2 e2 ∗ 3 e1  2 e2 
                       vv = 9 e1 e1  6 e1 e2  6 e2 e1  4 e2 e2

   e2
        e1                   e1 e1 = ?
                             e1 e2 = ?
    v =3, 2                e2 e1 = ?
    v =3 e12 e2             e2 e2 = ?
Gibbs (Vector Algebra)
                       vv = 9 e1 e1  6 e1 e2  6 e2 e1  4 e2 e2



                   v
                                    {   ei⋅e j = 1      i = j
                                    ei⋅e j = e j⋅ei = 0  i ≠ j 

   e2
        e1
                       vv = 9  4 = 13
    v =3, 2
                       v ⋅v = 3,2 ∗ 3,2
    v =3 e12 e2       v⋅ v = 3 ∗ 3  2 ∗ 2        dot product

                       v ⋅ v = 13
Clifford (Geometric Algebra)
                       vv = 9 e1 e1  6 e1 e2  6 e2 e1  4 e2 e2



                   v
                                  {    ei ⋅e j = 1     i = j
                                   ei⋅e j  e j⋅ei = 0  i ≠ j 

   e2
        e1                   ei ⋅e j = −e j⋅e i      Anticomutivity

    v =3, 2
                       vv = 9 e1 e1  6 e1 e2 − 6 e1 e2  4 e2 e2
    v =3 e12 e2
                       vv = 9  4 = 13

                         Inner product
The Goal: Outer Product
• What's e1e2 ?
                                         e2
   – It isn't a scalar
   – It isn't a vector
                                         O               e1
                                              e1 ∧ e 2
                                                         2D
• It's a plane, a vector space (blade)                   CCW
                                         e2
• Called Bivector or 2-vector

                                         O               e1
• Outer products span                         e2 ∧ e 1
Inner product (dot)
• Outer product spans

                 1 e1 ∧  2 e2 =  1 ∗  2 e1 e2

                v
                        vv = 9 e1 e1  6 e1 e2 − 6 e1 e2  4 e2 e2

  e2
                        vv = 9  4 = 13
       e1


• Inner product projects
                     12 e1 e2 ⋅ e2 = 12 e1
Geometric Product
                     v
                               vv = 9 e 1 e 1  6 e 1 e 2 − 6 e 1 e 2  4 e 2 e 2

    e2
                               vv = 9  4 = 13
         e1


{      e i ⋅e j = 1      i = j 
 e i ⋅e j  e j ⋅e i = 0  i ≠ j 
                                                   v ∥u
                                                  v ⊥u
                                                            
                                                            
                                                                 v ∧ u = 0
                                                                 v ⋅ u = 0

                                vu = v ⋅u  v ∧ u


                         Inner product           Outer product
Contraction Inner Product
• Generalizes inner product to Blades
• Complement of the orthogonal projection

                                        a


               a B=
                               a B

                                            B

• Generalizing geometric product

                      vu = v    uv ∧ u
History of Geometric Algebra
                               Synthetic Geometry
300 BC                               Euclid

         Analitic Geometric
1637        Descartes

1798     Complex Álgebra                            1844
          Wessel, Gauss        1843                   Exterior Algebra
                                Quaternions              Grassman
                                 Hamilton
1854     Matrix Álgebra
            Cayley               1881                         1878
1878     Determinants             Vector Algebra     Clifford Algebra
           Sylvester                  Gibbs               Clifford

                  1890 Tensor Algebra                          1923
                                                                 Differential Forms
                              Ricci
1928     Spin Algebra                                                 E. Cartan
         Pauli, Dirac
                                          Geometric Algebra
Multivector
• Unique structure


      v ℜ3=                                  Scalar
              e 1   e2   e 3            Vectors
              e 1 e2   e1 e 3   e2 e3   2-Blades
              e 1 e2 e3                     3-Blades




• Outer product spans
• Inner product projects
Operations on Multivectors
• Graduated Involution

                  v k = −1k ∗ v k
                  

• Reverse
                  v k = −1k k − 1/ 2 ∗ v k
                                                   vu = uv
                                                     


• Conjugation
                        
                        
                  v k =v =v

• Inverse
                             v
                  v −1 =
                           ∣v ∣ ²
Operations on Multivectors (cont)
• Angle between vectors
                                     v ⋅u
                          cos  =
                                    ∣ ∣∣ ∣
                                     v u

• Pseudoscalar
   – Dimensional limit

                         ps ℜ 3=1∗e 1∧e 2 ∧e 3


• Dual
                            A= Ã k∗ ps
Operations on Multivectors (cont 2)
• Meet
             C =A∩B

• Join
             C =A∪B

• Sum and difference

             C =AB        C = A− B

• Nabla = Symmetric difference

             v 1 ∇ v 2=majorGrade v 1∗v 2 
Future Readings              [Dorst, 02a] [Vaz, 97]


• Projection of blades and Rejection

• Reflection

• Rotors

• Models
  – Homogeneous model / Plücker coordinates
  – Conformal model

• Quaternions
GA Frameworks
• GAViewer: Geometric algebra computations and visualize

• GAP: Geometric Algebra Package [Zaharia, 03]

• GAIGEN: Code generator to Geometric Algebra [Fontijne]

• GluCat: template classes to Clifford algebras

• GAGL: Geometric Algebra to OpenGL

• GEOMA: C++ Template Classes for Geometric Algebras
Multivector Implementations
• GAGL
                                          scalar , e 1 , e 2 , e 3 ,
   – Vector[8]                         e 1 ∧e 2 , e 1∧e 3 , e 2 ∧e 3 ,
   – Only in 3D.                                 e 1∧e 2 ∧e 3

• GEOMA
   – Matrix [2k][2k] where k = grade

• GluCat: ??

• GAP     scalar , e 1 , e 2 , e 3 , e 1 ∧e 2 , e 1∧e 3 , e 2 ∧e 3 , e 1∧e 2 ∧e 3
                                    EBLADE              EBLADE       EBLADE

                        HMV                    HMV                       HMV
Clean Multivector Implementation
• Think OO with low memory usage

• Two Classes
   – GAMultivector
   – GASpace

• Inside GAMultivector
   – double[length]: where length is a compile time method

                                 n
                      length=1∑ C n , k
                                k =1
Performance Aspects
• Raytracer benchmark
                                             (Fontijne, D. & Dorst,2003)

           Model Implem. Full Rend Time (s) Memory (MB)
           3DLA Standard        1,00           6,2
           3DGA Gaigen          2,56           6,7
           4DLA Standard        1,05           6,4
           4DGA Gaigen          2,97           7,7
           5DGA Gaigen          5,71           9,9
                                           FAST
•    3DLA: Linear Algebra
•    3DGA: Geometric Algebra
                                     ELEGANCE
•    4DLA: Homogeneous coordinates
•    4DGA: Homogeneous model
•    5DGA: Conformal model
So...


“... that it provides a single, simple mathematical framework
    which eliminates the plethora of diverse mathematical
               descriptions and techniques...”


                                   [McRobie and Lasenby, 1999]
References
•   Dorst, L. & Mann, S. Geometric algebra: a computational framework for geometrical
    applications (part II: aplications) IEEE Computer Graphics and Applications, 2002, 1
•   Dorst, L. & Mann, S. Geometric algebra: a computational framework for geometrical
    applications (part I: algebra) IEEE Computer Graphics and Applications, 2002, 1, 24-31
•   Fontijne, D. & Dorst, L. Modeling 3D Euclidean Geometry IEEE Computer Graphics and
    Applications, 2003
•   Macdonald, A. A Survey of Geometric Algebra and Geometric Calculus, 2005
•   Vaz, J.J. A álgebra geométrica do espaço euclidiano e a teoria de Pauli Revista
    Brasileira de Ensino de Física, 1997, 19, 234-259
•   Zaharia, M.D. & Dorst, L. The Interface Spec. and Implementation Internals of a
    Program Module for Geometric Algebra University of Amsterdam, 2003

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Introduction about Geometric Algebra

  • 2. Cross Product in nD? • Why not? – V1 = (1.0, 2.0, 3.0, 4.0) – V2 = (-2.0, -3.0, -4.0, -5.0) V1×V2=? ? I J K Q 1,00 2,00 3,00 4,00 = ??? -2,00 -3,00 -4,00 -5,00
  • 3. A language for geometry Geometric Algebraic Concepts Language • Magnitude - size 3D 4,20 • Direction -orientation • Sense - negative/positive 35º • Grade - dimension CCW x
  • 4. What we need? • n-dimensional geometric language • Represent the object grade • Operate across different dimensions • Geometrically intuitive • Without limits • Coordinate free • Efficient • Unify other algebras
  • 5. Product of Vectors ? vv = ? v vv = 3 e1  2 e2 ∗ 3 e1  2 e2  vv = 9 e1 e1  6 e1 e2  6 e2 e1  4 e2 e2 e2 e1 e1 e1 = ? e1 e2 = ? v =3, 2 e2 e1 = ? v =3 e12 e2 e2 e2 = ?
  • 6. Gibbs (Vector Algebra) vv = 9 e1 e1  6 e1 e2  6 e2 e1  4 e2 e2 v { ei⋅e j = 1 i = j ei⋅e j = e j⋅ei = 0  i ≠ j  e2 e1 vv = 9  4 = 13 v =3, 2 v ⋅v = 3,2 ∗ 3,2 v =3 e12 e2 v⋅ v = 3 ∗ 3  2 ∗ 2 dot product v ⋅ v = 13
  • 7. Clifford (Geometric Algebra) vv = 9 e1 e1  6 e1 e2  6 e2 e1  4 e2 e2 v { ei ⋅e j = 1 i = j ei⋅e j  e j⋅ei = 0  i ≠ j  e2 e1 ei ⋅e j = −e j⋅e i Anticomutivity v =3, 2 vv = 9 e1 e1  6 e1 e2 − 6 e1 e2  4 e2 e2 v =3 e12 e2 vv = 9  4 = 13 Inner product
  • 8. The Goal: Outer Product • What's e1e2 ? e2 – It isn't a scalar – It isn't a vector O e1 e1 ∧ e 2 2D • It's a plane, a vector space (blade) CCW e2 • Called Bivector or 2-vector O e1 • Outer products span e2 ∧ e 1
  • 9. Inner product (dot) • Outer product spans  1 e1 ∧  2 e2 =  1 ∗  2 e1 e2 v vv = 9 e1 e1  6 e1 e2 − 6 e1 e2  4 e2 e2 e2 vv = 9  4 = 13 e1 • Inner product projects  12 e1 e2 ⋅ e2 = 12 e1
  • 10. Geometric Product v vv = 9 e 1 e 1  6 e 1 e 2 − 6 e 1 e 2  4 e 2 e 2 e2 vv = 9  4 = 13 e1 { e i ⋅e j = 1 i = j  e i ⋅e j  e j ⋅e i = 0  i ≠ j  v ∥u v ⊥u   v ∧ u = 0 v ⋅ u = 0 vu = v ⋅u  v ∧ u Inner product Outer product
  • 11. Contraction Inner Product • Generalizes inner product to Blades • Complement of the orthogonal projection a a B= a B B • Generalizing geometric product vu = v uv ∧ u
  • 12. History of Geometric Algebra Synthetic Geometry 300 BC Euclid Analitic Geometric 1637 Descartes 1798 Complex Álgebra 1844 Wessel, Gauss 1843 Exterior Algebra Quaternions Grassman Hamilton 1854 Matrix Álgebra Cayley 1881 1878 1878 Determinants Vector Algebra Clifford Algebra Sylvester Gibbs Clifford 1890 Tensor Algebra 1923 Differential Forms Ricci 1928 Spin Algebra E. Cartan Pauli, Dirac Geometric Algebra
  • 13. Multivector • Unique structure v ℜ3=  Scalar   e 1   e2   e 3 Vectors   e 1 e2   e1 e 3   e2 e3 2-Blades   e 1 e2 e3 3-Blades • Outer product spans • Inner product projects
  • 14. Operations on Multivectors • Graduated Involution v k = −1k ∗ v k  • Reverse v k = −1k k − 1/ 2 ∗ v k   vu = uv  • Conjugation     v k =v =v • Inverse v v −1 = ∣v ∣ ²
  • 15. Operations on Multivectors (cont) • Angle between vectors v ⋅u cos  = ∣ ∣∣ ∣ v u • Pseudoscalar – Dimensional limit ps ℜ 3=1∗e 1∧e 2 ∧e 3 • Dual A= Ã k∗ ps
  • 16. Operations on Multivectors (cont 2) • Meet C =A∩B • Join C =A∪B • Sum and difference C =AB C = A− B • Nabla = Symmetric difference v 1 ∇ v 2=majorGrade v 1∗v 2 
  • 17. Future Readings [Dorst, 02a] [Vaz, 97] • Projection of blades and Rejection • Reflection • Rotors • Models – Homogeneous model / Plücker coordinates – Conformal model • Quaternions
  • 18. GA Frameworks • GAViewer: Geometric algebra computations and visualize • GAP: Geometric Algebra Package [Zaharia, 03] • GAIGEN: Code generator to Geometric Algebra [Fontijne] • GluCat: template classes to Clifford algebras • GAGL: Geometric Algebra to OpenGL • GEOMA: C++ Template Classes for Geometric Algebras
  • 19. Multivector Implementations • GAGL scalar , e 1 , e 2 , e 3 , – Vector[8] e 1 ∧e 2 , e 1∧e 3 , e 2 ∧e 3 , – Only in 3D. e 1∧e 2 ∧e 3 • GEOMA – Matrix [2k][2k] where k = grade • GluCat: ?? • GAP scalar , e 1 , e 2 , e 3 , e 1 ∧e 2 , e 1∧e 3 , e 2 ∧e 3 , e 1∧e 2 ∧e 3 EBLADE EBLADE EBLADE HMV HMV HMV
  • 20. Clean Multivector Implementation • Think OO with low memory usage • Two Classes – GAMultivector – GASpace • Inside GAMultivector – double[length]: where length is a compile time method n length=1∑ C n , k k =1
  • 21. Performance Aspects • Raytracer benchmark (Fontijne, D. & Dorst,2003) Model Implem. Full Rend Time (s) Memory (MB) 3DLA Standard 1,00 6,2 3DGA Gaigen 2,56 6,7 4DLA Standard 1,05 6,4 4DGA Gaigen 2,97 7,7 5DGA Gaigen 5,71 9,9 FAST • 3DLA: Linear Algebra • 3DGA: Geometric Algebra ELEGANCE • 4DLA: Homogeneous coordinates • 4DGA: Homogeneous model • 5DGA: Conformal model
  • 22. So... “... that it provides a single, simple mathematical framework which eliminates the plethora of diverse mathematical descriptions and techniques...” [McRobie and Lasenby, 1999]
  • 23. References • Dorst, L. & Mann, S. Geometric algebra: a computational framework for geometrical applications (part II: aplications) IEEE Computer Graphics and Applications, 2002, 1 • Dorst, L. & Mann, S. Geometric algebra: a computational framework for geometrical applications (part I: algebra) IEEE Computer Graphics and Applications, 2002, 1, 24-31 • Fontijne, D. & Dorst, L. Modeling 3D Euclidean Geometry IEEE Computer Graphics and Applications, 2003 • Macdonald, A. A Survey of Geometric Algebra and Geometric Calculus, 2005 • Vaz, J.J. A álgebra geométrica do espaço euclidiano e a teoria de Pauli Revista Brasileira de Ensino de Física, 1997, 19, 234-259 • Zaharia, M.D. & Dorst, L. The Interface Spec. and Implementation Internals of a Program Module for Geometric Algebra University of Amsterdam, 2003