Utente:Grasso Luigi/sandbox4/Algebra geometrica

L'algebra geometrica (GA) di uno spazio vettoriale è un'algebra su campo, e definisce un'operazione di moltiplicazione detta prodotto geometrico su uno spazio di elementi detti multivettori, che contiene sia scalari $F$ e uno spazio vettoriale $V$. Matematicamente, un'algebra geometrica si definisce come l'algebra di Clifford di uno spazio vettoriale con una forma quadratica. Il contributo di Clifford è stato quello di definire un nuovo prodotto, il prodotto geometrico, unificando l'algebra di Grassmann (o esterna o antisimmetrica dotata del prodotto wedge) e l'algebra di Hamilton in una struttura unica. Aggiungendo il duale del prodotto esterno (il "meet") otteniamo l'algebra di Grassmann–Cayley, e una versione conforme di quest'ultimo insieme ad un'algebra di Clifford conforme crea un'algebra geometrica conforme(CGA) che trova applicazione nelle geometrie classiche. In pratica, questi e molte operazioni derivate permettono una corrispondenza di elementi, sottospazi e operazioni dell'algebra ordinaria con interpretazioni geometriche.

Gli scalari e i vettori sono quelli ordinari, e formano sottospazi distinti di una GA. I bivettori forniscono una rappresentazione più naturale delle quantità pseudovettori dell'algebra vettoriale come area orientata, angolo di rotazione orientato, torque, momento angolare, campo elettromagnetico e il vettore di Poynting. Un trivettore fornisce una rappresentazione di un volume orientato, e così via. Un elemento detto blade permette di rappresentare un sottospazio di $V$ e le proiezioni ortogonali in questo sottospazio. Le rotazioni e le riflessioni sono rappresentati come elementi. A differenza dell'algebra vettoriale, una GA permette di usare qualsiasi numero di dimensioni e qualsiasi forma quadratica come quella utilizzata in relatività.

Esempi di algebre geometriche applicate alla fisica includono l'algebra spaziotempo (e meno comunemente l'algebra dello spazio fisico) e l'algebra geometrica conforme. Il calcolo geometrico, estensione della GA con le operazioni di differenziazione e integrazione, si utilizza nello studio di altre teorie come l'analisi complessa, la geometria differenziale, e.g. usando l'algebra di Clifford al posto delle forme differenziali. L'algebra geometrica è stata sostenuta, in particolare da David O. Hestenes e Chris Doran, come lo strumento matematico preferito per la fisica. I sostenitori affermano che fornisce descrizioni intuitive e compatte in molte aree tra cui la meccanica quantistica e classica, la teoria elettromagnetica e la relatività. GA ha pure applicazioni come strumento di elaborazione in computer grafica e nella robotica.

Il prodotto geometrico fu menzionato per primo da Hermann Grassmann, che era principalmente interessato allo sviluppo dell'algebra esterna strettamente correlata. Nel 1878, William Kingdon Clifford ha notevolmente ampliato il lavoro di Grassmann per formare quelle che sono di solito chiamate algebre di Clifford in suo onore (sebbene fu lo stesso Clifford a chiamarle "algebre geometriche"). Per diversi decenni, le algebre geometriche sono state in qualche modo ignorate, molto eclissate dal calcolo vettoriale sviluppato di recente per descrivere l'elettromagnetismo. Il termine "algebra geometrica" ​​fu approfondito negli anni '60 da Hestenes, che ne ha sostenuto la sua importanza per la fisica relativistica.

Definizioni e notazioni

Vi sono differenti modi per definire un'algebra geometrica. L'approccio originale di Hestenes era assiomatico, "pieno di significato geometrico" ed equivalente all'algebra di Clifford universale.

Supponiamo uno spazio quadratico $V$ a dimensione finita sopra un campo $F$ con una forma bilineare simmetrica (il prodotto interno, e.g. lo spazio quadratico Euclediano o la metrica lorentziana) $g:V\times V\rightarrow F$, l' algebra geometrica è quella di Clifford $\operatorname {Cl} (V,g)$. Come al solito in questo contesto, per il resto della pagina, verà considerato solo il caso reale, $F=\mathbf {R}$. La notazione ${\mathcal {G}}(p,q)$ (oppure ${\mathcal {G}}(p,q,r)$) indica un'algebra geometrica per la quale la forma bilineare $g$ ha segnatura $(p,q)$ (oppure $(p,q,r)$).

Il prodotto essenziale dell'algebra è chiamato prodotto geometrico, e il prodotto nell'algebra esterna contained viene detto prodotto esteriore (solitamente si usa il termine prodotto esterno e meno spesso prodotto wedge). È normale denotarlo per accostamento (ad esempio, sopprimendo qualsiasi simbolo esplicito di moltiplicazione) e usare il simbolo $\wedge$. La definizione data di algebra geometrica è astratta, quindi riassumiamo le proprietà del prodotto geometrico tramite il seguente insieme di assiomi. Fissati $A,B,C\in {\mathcal {G}}(p,q)$, abbiamo:

$AB\in {\mathcal {G}}(p,q)$ (chiusura)

$1A=A1=A$, dove $1$ è l'elemento identità (esistenza dell'elemento identità)

$A(BC)=(AB)C$ (associatività)

$A(B+C)=AB+AC$ and $(B+C)A=BA+CA$ (distributività)

$a^{2}=g(a,a)1$, dove $a$ è qualunque elemento del sottospazio $V$ dell'algebra.

Il prodotto esteriore ha stesse proprietà, tranne per l'ultima proprietà sopra sostituita da$a\wedge a=0$ per $a\in V$. Note that in the last property above, the real number $g(a,a)$ need not be nonnegative if $g$ is not positive-definite. An important property of the geometric product is the existence of elements having a multiplicative inverse. For a vector $a$, if $a^{2}\neq 0$ then $a^{-1}$ exists and is equal to $g(a,a)^{-1}a$. A nonzero element of the algebra does not necessarily have a multiplicative inverse. For example, if $u$ is a vector in $V$ such that $u^{2}=1$, the element $\textstyle {\frac {1}{2}}(1+u)$ is both a nontrivial idempotent element and a nonzero zero divisor, and thus has no inverse.

It is usual to identify $\mathbf {R}$ and $V$ with their images under the natural embeddings $\mathbf {R} \to {\mathcal {G}}(p,q)$ and $V\to {\mathcal {G}}(p,q)$. In this article, this identification is assumed. Throughout, the terms scalar and vector refer to elements of $\mathbf {R}$ and $V$ respectively (and of their images under this embedding).

Il prodotto geometrico

Dati due vettori $a$ and $b$, se il prodotto geometrico $ab$ è anticommutativo, sono perpendicolari (in alto) perchè $a\cdot b=0$; se è commutativo, sono paralleli (in basso) perchè $a\wedge b=0$.

Orientation defined by an ordered set of vectors.

Reversed orientation corresponds to negating the exterior product.

Geometric interpretation of grade-$n$ elements in a real exterior algebra for $n=0$ (signed point), $1$ (directed line segment, or vector), $2$ (oriented plane element), $3$ (oriented volume). The exterior product of $n$ vectors can be visualized as any $n$-dimensional shape (e.g. $n$-parallelotope, $n$-ellipsoid); with magnitude (hypervolume), and orientation defined by that on its $(n-1)$-dimensional boundary and on which side the interior is.Template:SfnTemplate:Sfn

For vectors $a$ and $b$, we may write the geometric product of any two vectors $a$ and $b$ as the sum of a symmetric product and an antisymmetric product:

$ab={\frac {1}{2}}(ab+ba)+{\frac {1}{2}}(ab-ba)$

Dunque noi possiano definire il prodotto interno di vettori come

$a\cdot b:=g(a,b),$

e quindi il prodotto simmetrico può esplicitarsi

${\frac {1}{2}}(ab+ba)={\frac {1}{2}}\left((a+b)^{2}-a^{2}-b^{2}\right)=a\cdot b$

Conversely, $g$ is completely determined by the algebra. The antisymmetric part is the exterior product of the two vectors, the product of the contained exterior algebra:

$a\wedge b:={\frac {1}{2}}(ab-ba)=-(b\wedge a)$

Then by simple addition:

$ab=a\cdot b+a\wedge b$ the ungeneralized or vector form of the geometric product.

The inner and exterior products are associated with familiar concepts from standard vector algebra. Geometrically, $a$ and $b$ are parallel if their geometric product is equal to their inner product, whereas $a$ and $b$ are perpendicular if their geometric product is equal to their exterior product. In a geometric algebra for which the square of any nonzero vector is positive, the inner product of two vectors can be identified with the dot product of standard vector algebra. The exterior product of two vectors can be identified with the signed area enclosed by a parallelogram the sides of which are the vectors. The cross product of two vectors in $3$ dimensions with positive-definite quadratic form is closely related to their exterior product.

Most instances of geometric algebras of interest have a nondegenerate quadratic form. If the quadratic form is fully degenerate, the inner product of any two vectors is always zero, and the geometric algebra is then simply an exterior algebra. Unless otherwise stated, this article will treat only nondegenerate geometric algebras.

The exterior product is naturally extended as an associative bilinear binary operator between any two elements of the algebra, satisfying the identities

${\begin{aligned}1\wedge a_{i}&=a_{i}\wedge 1=a_{i}\\a_{1}\wedge a_{2}\wedge \cdots \wedge a_{r}&={\frac {1}{r!}}\sum _{\sigma \in {\mathfrak {S}}_{r}}\operatorname {sgn} (\sigma )a_{\sigma (1)}a_{\sigma (2)}\cdots a_{\sigma (r)},\end{aligned}}$

where the sum is over all permutations of the indices, with $\operatorname {sgn} (\sigma )$ the sign of the permutation, and $a_{i}$ are vectors (not general elements of the algebra). Since every element of the algebra can be expressed as the sum of products of this form, this defines the exterior product for every pair of elements of the algebra. It follows from the definition that the exterior product forms an alternating algebra.

Blade, grade, e basi canoniche

A multivector that is the exterior product of $r$ linearly independent vectors is called a blade, and is said to be of grade $r$. A multivector that is the sum of blades of grade $r$ is called a (homogeneous) multivector of grade $r$. From the axioms, with closure, every multivector of the geometric algebra is a sum of blades.

Consider a set of $r$ linearly independent vectors $\{a_{1},\ldots ,a_{r}\}$ spanning an $r$-dimensional subspace of the vector space. With these, we can define a real symmetric matrix (in the same way as a Gramian matrix)

$[\mathbf {A} ]_{ij}=a_{i}\cdot a_{j}$

By the spectral theorem, $\mathbf {A}$ can be diagonalized to diagonal matrix $\mathbf {D}$ by an orthogonal matrix $\mathbf {O}$ via

$\sum _{k,l}[\mathbf {O} ]_{ik}[\mathbf {A} ]_{kl}[\mathbf {O} ^{\mathrm {T} }]_{lj}=\sum _{k,l}[\mathbf {O} ]_{ik}[\mathbf {O} ]_{jl}[\mathbf {A} ]_{kl}=[\mathbf {D} ]_{ij}$

Define a new set of vectors $\{e_{1},\ldots ,e_{r}\}$, known as orthogonal basis vectors, to be those transformed by the orthogonal matrix:

$e_{i}=\sum _{j}[\mathbf {O} ]_{ij}a_{j}$

Since orthogonal transformations preserve inner products, it follows that $e_{i}\cdot e_{j}=[\mathbf {D} ]_{ij}$ and thus the $\{e_{1},\ldots ,e_{r}\}$ are perpendicular. In other words, the geometric product of two distinct vectors $e_{i}\neq e_{j}$ is completely specified by their exterior product, or more generally

${\begin{array}{rl}e_{1}e_{2}\cdots e_{r}&=e_{1}\wedge e_{2}\wedge \cdots \wedge e_{r}\\&=\left(\sum _{j}[\mathbf {O} ]_{1j}a_{j}\right)\wedge \left(\sum _{j}[\mathbf {O} ]_{2j}a_{j}\right)\wedge \cdots \wedge \left(\sum _{j}[\mathbf {O} ]_{rj}a_{j}\right)\\&=(\det \mathbf {O} )a_{1}\wedge a_{2}\wedge \cdots \wedge a_{r}\end{array}}$

Therefore, every blade of grade $r$ can be written as a geometric product of $r$ vectors. More generally, if a degenerate geometric algebra is allowed, then the orthogonal matrix is replaced by a block matrix that is orthogonal in the nondegenerate block, and the diagonal matrix has zero-valued entries along the degenerate dimensions. If the new vectors of the nondegenerate subspace are normalized according to

${\hat {e}}_{i}={\frac {1}{\sqrt {|e_{i}\cdot e_{i}|}}}e_{i},$

then these normalized vectors must square to $+1$ or $-1$. By Sylvester's law of inertia, the total number of $+1$s and the total number of $-1$s along the diagonal matrix is invariant. By extension, the total number $p$ of these vectors that square to $+1$ and the total number $q$ that square to $-1$ is invariant. (The total number of basis vectors that square to zero is also invariant, and may be nonzero if the degenerate case is allowed.) We denote this algebra ${\mathcal {G}}(p,q)$. For example, ${\mathcal {G}}(3,0)$ models $3$-dimensional Euclidean space, ${\mathcal {G}}(1,3)$ relativistic spacetime and ${\mathcal {G}}(4,1)$ a conformal geometric algebra of a $3$-dimensional space.

The set of all possible products of $n$ orthogonal basis vectors with indices in increasing order, including $1$ as the empty product, forms a basis for the entire geometric algebra (an analogue of the PBW theorem). For example, the following is a basis for the geometric algebra ${\mathcal {G}}(3,0)$:

$\{1,e_{1},e_{2},e_{3},e_{1}e_{2},e_{1}e_{3},e_{2}e_{3},e_{1}e_{2}e_{3}\}$

A basis formed this way is called a canonical basis for the geometric algebra, and any other orthogonal basis for $V$ will produce another canonical basis. Each canonical basis consists of $2^{n}$ elements. Every multivector of the geometric algebra can be expressed as a linear combination of the canonical basis elements. If the canonical basis elements are $\{B_{i}|i\in S\}$ with $S$ being an index set, then the geometric product of any two multivectors is

$\left(\sum _{i}\alpha _{i}B_{i}\right)\left(\sum _{j}\beta _{j}B_{j}\right)=\sum _{i,j}\alpha _{i}\beta _{j}B_{i}B_{j}.$

The terminology "$k$-vector" is often encountered to describe multivectors containing elements of only one grade. In higher dimensional space, some such multivectors are not blades (cannot be factored into the exterior product of $k$ vectors). By way of example, $e_{1}\wedge e_{2}+e_{3}\wedge e_{4}$ in ${\mathcal {G}}(4,0)$ cannot be factored; typically, however, such elements of the algebra do not yield to geometric interpretation as objects, although they may represent geometric quantities such as rotations. Only $0,1,(n-1)$ and $n$-vectors are always blades in $n$-space.

Grade projection

Using an orthogonal basis, a graded vector space structure can be established. Elements of the geometric algebra that are scalar multiples of $1$ are grade-$0$ blades and are called scalars. Multivectors that are in the span of $\{e_{1},\ldots ,e_{n}\}$ are grade-$1$ blades and are the ordinary vectors. Multivectors in the span of $\{e_{i}e_{j}\mid 1\leq i<j\leq n\}$ are grade-$2$ blades and are the bivectors. This terminology continues through to the last grade of $n$-vectors. Alternatively, grade-$n$ blades are called pseudoscalars, grade-$(n-1)$ blades pseudovectors, etc. Many of the elements of the algebra are not graded by this scheme since they are sums of elements of differing grade. Such elements are said to be of mixed grade. The grading of multivectors is independent of the basis chosen originally.

This is a grading as a vector space, but not as an algebra. Because the product of an $r$-blade and an $s$-blade is contained in the span of $0$ through $r+s$-blades, the geometric algebra is a filtered algebra.

A multivector $A$ may be decomposed with the grade-projection operator $\langle A\rangle _{r}$, which outputs the grade-$r$ portion of $A$. As a result:

$A=\sum _{r=0}^{n}\langle A\rangle _{r}$

As an example, the geometric product of two vectors $ab=a\cdot b+a\wedge b=\langle ab\rangle _{0}+\langle ab\rangle _{2}$ since $\langle ab\rangle _{0}=a\cdot b$ and $\langle ab\rangle _{2}=a\wedge b$ and $\langle ab\rangle _{i}=0$, for $i$ other than $0$ and $2$.

The decomposition of a multivector $A$ may also be split into those components that are even and those that are odd:

$A^{+}=\langle A\rangle _{0}+\langle A\rangle _{2}+\langle A\rangle _{4}+\cdots$

$A^{-}=\langle A\rangle _{1}+\langle A\rangle _{3}+\langle A\rangle _{5}+\cdots$

This is the result of forgetting structure from a $\mathrm {Z}$-graded vector space to $\mathrm {Z} _{2}$-graded vector space. The geometric product respects this coarser grading. Thus in addition to being a $\mathrm {Z} _{2}$-graded vector space, the geometric algebra is a $\mathrm {Z} _{2}$-graded algebra or superalgebra.

Restricting to the even part, the product of two even elements is also even. This means that the even multivectors defines an even subalgebra. The even subalgebra of an $n$-dimensional geometric algebra is isomorphic (without preserving either filtration or grading) to a full geometric algebra of $(n-1)$ dimensions. Examples include ${\mathcal {G}}^{+}(2,0)\cong {\mathcal {G}}(0,1)$ and ${\mathcal {G}}^{+}(1,3)\cong {\mathcal {G}}(3,0)$.

Representation of subspaces

Geometric algebra represents subspaces of $V$ as blades, and so they coexist in the same algebra with vectors from $V$. A $k$-dimensional subspace $W$ of $V$ is represented by taking an orthogonal basis $\{b_{1},b_{2},\ldots ,b_{k}\}$ and using the geometric product to form the blade $D=b_{1}b_{2}\cdots b_{k}$. There are multiple blades representing $W$; all those representing $W$ are scalar multiples of $D$. These blades can be separated into two sets: positive multiples of $D$ and negative multiples of $D$. The positive multiples of $D$ are said to have the same orientation as $D$, and the negative multiples the opposite orientation.

Blades are important since geometric operations such as projections, rotations and reflections depend on the factorability via the exterior product that (the restricted class of) $n$-blades provide but that (the generalized class of) grade-$n$ multivectors do not when $n\geq 4$.

Unit pseudoscalars

Unit pseudoscalars are blades that play important roles in GA. A unit pseudoscalar for a non-degenerate subspace $W$ of $V$ is a blade that is the product of the members of an orthonormal basis for $W$. It can be shown that if $I$ and $I'$ are both unit pseudoscalars for $W$, then $I=\pm I'$ and $I^{2}=\pm 1$. If one doesn't choose an orthonormal basis for $W$, then the Plucker embedding gives a vector in the exterior algebra but only up to scaling. Using the vector space isomorphism between the geometric algebra and exterior algebra, this gives the equivalence class of $\alpha I$ for all $\alpha \neq 0$. Orthonormality gets rid of this ambiguity except for the signs above.

Suppose the geometric algebra ${\mathcal {G}}(n,0)$ with the familiar positive definite inner product on $\mathbf {R} ^{n}$ is formed. Given a plane ($2$-dimensional subspace) of $\mathbf {R} ^{n}$, one can find an orthonormal basis $\{b_{1},b_{2}\}$ spanning the plane, and thus find a unit pseudoscalar $I=b_{1}b_{2}$ representing this plane. The geometric product of any two vectors in the span of $b_{1}$ and $b_{2}$ lies in $\{\alpha _{0}+\alpha _{1}I\mid \alpha _{i}\in \mathbf {R} \}$, that is, it is the sum of a $0$-vector and a $2$-vector.

By the properties of the geometric product, $I^{2}=b_{1}b_{2}b_{1}b_{2}=-b_{1}b_{2}b_{2}b_{1}=-1$. The resemblance to the imaginary unit is not incidental: the subspace $\{\alpha _{0}+\alpha _{1}I\mid \alpha _{i}\in \mathbf {R} \}$ is $\mathbf {R}$-algebra isomorphic to the complex numbers. In this way, a copy of the complex numbers is embedded in the geometric algebra for each 2-dimensional subspace of $V$ on which the quadratic form is definite.

It is sometimes possible to identify the presence of an imaginary unit in a physical equation. Such units arise from one of the many quantities in the real algebra that square to $-1$, and these have geometric significance because of the properties of the algebra and the interaction of its various subspaces.

In ${\mathcal {G}}(3,0)$, a further familiar case occurs. Given a canonical basis consisting of orthonormal vectors $e_{i}$ of $V$, the set of all $2$-vectors is spanned by

$\{e_{3}e_{2},e_{1}e_{3},e_{2}e_{1}\}.$

Labelling these $i$, $j$ and $k$ (momentarily deviating from our uppercase convention), the subspace generated by $0$-vectors and $2$-vectors is exactly $\{\alpha _{0}+i\alpha _{1}+j\alpha _{2}+k\alpha _{3}\mid \alpha _{i}\in \mathbf {R} \}$. This set is seen to be the even subalgebra of ${\mathcal {G}}(3,0)$, and furthermore is isomorphic as an $\mathbf {R}$-algebra to the quaternions, another important algebraic system.

Dual basis

Let $\{e_{1},\ldots ,e_{n}\}$ be a basis of $V$, i.e. a set of $n$ linearly independent vectors that span the $n$-dimensional vector space $V$. The basis that is dual to $\{e_{1},\ldots ,e_{n}\}$ is the set of elements of the dual vector space $V^{*}$ that forms a biorthogonal system with this basis, thus being the elements denoted $\{e^{1},\ldots ,e^{n}\}$ satisfying

$e^{i}\cdot e_{j}=\delta ^{i}{}_{j},$

where $\delta$ is the Kronecker delta.

Given a nondegenerate quadratic form on $V$, $V^{*}$ becomes naturally identified with $V$, and the dual basis may be regarded as elements of $V$, but are not in general the same set as the original basis.

Given further a GA of $V$, let

$\epsilon =e_{1}\wedge \cdots \wedge e_{n}$

be the pseudoscalar (which does not necessarily square to $\pm 1$) formed from the basis $\{e_{1},\ldots ,e_{n}\}$. The dual basis vectors may be constructed as

$e^{i}=(-1)^{i-1}(e_{1}\wedge \cdots \wedge {\check {e}}_{i}\wedge \cdots \wedge e_{n})\epsilon ^{-1},$

where the ${\check {e}}_{i}$ denotes that the $i$th basis vector is omitted from the product.

Extensions of the inner and exterior products

It is common practice to extend the exterior product on vectors to the entire algebra. This may be done through the use of the grade projection operator:

$C\wedge D:=\sum _{r,s}\langle \langle C\rangle _{r}\langle D\rangle _{s}\rangle _{r+s}$     (the exterior product)

This generalization is consistent with the above definition involving antisymmetrization. Another generalization related to the exterior product is the commutator product:

$C\times D:={\tfrac {1}{2}}(CD-DC)$     (the commutator product)

The regressive product (usually referred to as the "meet") is the dual of the exterior product (or "join" in this context). The dual specification of elements permits, for blades $A$ and $B$, the intersection (or meet) where the duality is to be taken relative to the smallest grade blade containing both $A$ and $B$ (the join).Template:Sfn

$C\vee D:=((CI^{-1})\wedge (DI^{-1}))I$

with $I$ the unit pseudoscalar of the algebra. The regressive product, like the exterior product, is associative.Template:Sfn

The inner product on vectors can also be generalized, but in more than one non-equivalent way. The paper Template:Harvard citation gives a full treatment of several different inner products developed for geometric algebras and their interrelationships, and the notation is taken from there. Many authors use the same symbol as for the inner product of vectors for their chosen extension (e.g. Hestenes and Perwass). No consistent notation has emerged.

Among these several different generalizations of the inner product on vectors are:

$C\;\rfloor \;D:=\sum _{r,s}\langle \langle C\rangle _{r}\langle D\rangle _{s}\rangle _{s-r}$   (the left contraction)

$C\;\lfloor \;D:=\sum _{r,s}\langle \langle C\rangle _{r}\langle D\rangle _{s}\rangle _{r-s}$   (the right contraction)

$C*D:=\sum _{r,s}\langle \langle C\rangle _{r}\langle D\rangle _{s}\rangle _{0}$   (the scalar product)

$C\bullet D:=\sum _{r,s}\langle \langle C\rangle _{r}\langle D\rangle _{s}\rangle _{|s-r|}$   (the "(fat) dot" product)

Template:Harvtxt makes an argument for the use of contractions in preference to Hestenes's inner product; they are algebraically more regular and have cleaner geometric interpretations. A number of identities incorporating the contractions are valid without restriction of their inputs. For example,

$C\;\rfloor \;D=(C\wedge (DI^{-1}))I$

$C\;\lfloor \;D=I((I^{-1}C)\wedge D)$

$(A\wedge B)*C=A*(B\;\rfloor \;C)$

$C*(B\wedge A)=(C\;\lfloor \;B)*A$

$A\;\rfloor \;(B\;\rfloor \;C)=(A\wedge B)\;\rfloor \;C$

$(A\;\rfloor \;B)\;\lfloor \;C=A\;\rfloor \;(B\;\lfloor \;C).$

Benefits of using the left contraction as an extension of the inner product on vectors include that the identity $ab=a\cdot b+a\wedge b$ is extended to $aB=a\;\rfloor \;B+a\wedge B$ for any vector $a$ and multivector $B$, and that the projection operation ${\mathcal {P}}_{b}(a)=(a\cdot b^{-1})b$ is extended to ${\mathcal {P}}_{B}(A)=(A\;\rfloor \;B^{-1})\;\rfloor \;B$ for any blade $B$ and any multivector $A$ (with a minor modification to accommodate null $B$, given below).

Linear functions

Although a versor is easier to work with because it can be directly represented in the algebra as a multivector, versors are a subgroup of linear functions on multivectors, which can still be used when necessary. The geometric algebra of an $n$-dimensional vector space is spanned by a basis of $2^{n}$ elements. If a multivector is represented by a $2^{n}\times 1$ real column matrix of coefficients of a basis of the algebra, then all linear transformations of the multivector can be expressed as the matrix multiplication by a $2^{n}\times 2^{n}$ real matrix. However, such a general linear transformation allows arbitrary exchanges among grades, such as a "rotation" of a scalar into a vector, which has no evident geometric interpretation.

A general linear transformation from vectors to vectors is of interest. With the natural restriction to preserving the induced exterior algebra, the outermorphism of the linear transformation is the unique extension of the versor. If $f$ is a linear function that maps vectors to vectors, then its outermorphism is the function that obeys the rule

${\underline {\mathsf {f}}}(a_{1}\wedge a_{2}\wedge \cdots \wedge a_{r})=f(a_{1})\wedge f(a_{2})\wedge \cdots \wedge f(a_{r})$

for a blade, extended to the whole algebra through linearity.

Modeling geometries

Although a lot of attention has been placed on CGA, it is to be noted that GA is not just one algebra, it is one of a family of algebras with the same essential structure.Template:Sfn

Vector space model

${\mathcal {G}}(3,0)$ may be considered as an extension or completion of vector algebra. From Vectors to Geometric Algebra covers basic analytic geometry and gives an introduction to stereographic projection.Template:Sfn

The even subalgebra of ${\mathcal {G}}(2,0)$ is isomorphic to the complex numbers, as may be seen by writing a vector $P$ in terms of its components in an orthonormal basis and left multiplying by the basis vector $e_{1}$, yielding

$Z=e_{1}P=e_{1}(xe_{1}+ye_{2})=x(1)+y(e_{1}e_{2}),$

where we identify $i\mapsto e_{1}e_{2}$ since

$({e_{1}}{e_{2}})^{2}={e_{1}}{e_{2}}{e_{1}}{e_{2}}=-{e_{1}}{e_{1}}{e_{2}}{e_{2}}=-1.$

Similarly, the even subalgebra of ${\mathcal {G}}(3,0)$ with basis $\{1,e_{2}e_{3},e_{3}e_{1},e_{1}e_{2}\}$ is isomorphic to the quaternions as may be seen by identifying $i\mapsto -e_{2}e_{3}$, $j\mapsto -e_{3}e_{1}$ and $k\mapsto -e_{1}e_{2}$.

Every associative algebra has a matrix representation; replacing the three Cartesian basis vectors by the Pauli matrices gives a representation of ${\mathcal {G}}(3,0)$:

${\begin{aligned}e_{1}=\sigma _{1}=\sigma _{x}&={\begin{pmatrix}0&1\\1&0\end{pmatrix}}\\e_{2}=\sigma _{2}=\sigma _{y}&={\begin{pmatrix}0&-i\\i&0\end{pmatrix}}\\e_{3}=\sigma _{3}=\sigma _{z}&={\begin{pmatrix}1&0\\0&-1\end{pmatrix}}\,.\end{aligned}}$

Dotting the "Pauli vector" (a dyad):

$\sigma =\sigma _{1}e_{1}+\sigma _{2}e_{2}+\sigma _{3}e_{3}$ with arbitrary vectors $a$ and $b$ and multiplying through gives:

$(\sigma \cdot a)(\sigma \cdot b)=a\cdot b+a\wedge b$ (Equivalently, by inspection, $a\cdot b+i\sigma \cdot$ ($a$ × $b$))

Spacetime model

In physics, the main applications are the geometric algebra of Minkowski 3+1 spacetime, ${\mathcal {G}}(1,3)$, called spacetime algebra (STA),Template:Sfn or less commonly, ${\mathcal {G}}(3,0)$, interpreted the algebra of physical space (APS).

While in STA points of spacetime are represented simply by vectors, in APS, points of $(3+1)$-dimensional spacetime are instead represented by paravectors: a $3$-dimensional vector (space) plus a $1$-dimensional scalar (time).

In spacetime algebra the electromagnetic field tensor has a bivector representation ${F}=({E}+ic{B})\gamma _{0}$. Here, the $i=\gamma _{0}\gamma _{1}\gamma _{2}\gamma _{3}$ is the unit pseudoscalar (or four-dimensional volume element), $\gamma _{0}$ is the unit vector in time direction, and $E$ and $B$ are the classic electric and magnetic field vectors (with a zero time component). Using the four-current ${J}$, Maxwell's equations then become

Formulation	Homogeneous equations	Non-homogeneous equations
Fields	$DF=\mu _{0}J$
	$D\wedge F=0$	$D~\rfloor ~F=\mu _{0}J$
Potentials (any gauge)	$F=D\wedge A$	$D~\rfloor ~(D\wedge A)=\mu _{0}J$
Potentials (Lorenz gauge)	$F=DA$ $D~\rfloor ~A=0$	$D^{2}A=\mu _{0}J$

In geometric calculus, juxtapositioning of vectors such as in $DF$ indicate the geometric product and can be decomposed into parts as $DF=D~\rfloor ~F+D\wedge F$. Here $D$ is the covector derivative in any spacetime and reduces to $\nabla$ in flat spacetime. Where $\bigtriangledown$ plays a role in Minkowski $4$-spacetime which is synonymous to the role of $\nabla$ in Euclidean $3$-space and is related to the d'Alembertian by $\Box =\bigtriangledown ^{2}$. Indeed, given an observer represented by a future pointing timelike vector $\gamma _{0}$ we have

$\gamma _{0}\cdot \bigtriangledown ={\frac {1}{c}}{\frac {\partial }{\partial t}}$

$\gamma _{0}\wedge \bigtriangledown =\nabla$

Boosts in this Lorentzian metric space have the same expression $e^{\beta }$ as rotation in Euclidean space, where ${\beta }$ is the bivector generated by the time and the space directions involved, whereas in the Euclidean case it is the bivector generated by the two space directions, strengthening the "analogy" to almost identity.

The Dirac matrices are a representation of ${\mathcal {G}}(1,3)$, showing the equivalence with matrix representations used by physicists.

Homogeneous model

The first model here is ${\mathcal {G}}(4,0)$, the GA version of homogeneous coordinates used in projective geometry. Here a vector represents a point and an outer product of vectors an oriented length yet we may work with the algebra in just the same way as in ${\mathcal {G}}(3,0)$. However, a useful inner product cannot be defined in the space and so there is no geometric product either leaving only outer product and non-metric uses of duality such as meet and join.

Nevertheless, there has been investigation of 4-dimensional alternatives to the full 5-dimensional CGA for limited geometries such as rigid body movements. A selection of these can be found in Part IV of Guide to Geometric Algebra in Practice.Template:Sfn Note that the algebra ${\mathcal {G}}(3,0,1)$ appears as a subalgebra of CGA by selecting just one null vector and dropping the other and further that the "motor algebra" (isomorphic to dual quaternions) is the even subalgebra of ${\mathcal {G}}(3,0,1)$.

Conformal model

A compact description of the current state of the art is provided by Template:Harvp, which also includes further references, in particular to Template:Harvp. Other useful references are Template:Harvp and Template:Harvp.

Working within GA, Euclidean space ${\mathcal {E}}^{3}$ (along with a conformal point at infinity) is embedded projectively in the CGA ${\mathcal {G}}(4,1)$ via the identification of Euclidean points with $1$-d subspaces in the $4$-d null cone of the $5$-d CGA vector subspace. This allows all conformal transformations to be done as rotations and reflections and is covariant, extending incidence relations of projective geometry to circles and spheres.

Specifically, we add orthogonal basis vectors $e_{+}$ and $e_{-}$ such that ${e_{+}}^{2}=+1$ and ${e_{-}}^{2}=-1$ to the basis of the vector space that generates ${\mathcal {G}}(3,0)$ and identify null vectors

$n_{\infty }=e_{-}+e_{+}$ as a conformal point at infinity (see Compactification) and

$n_{\text{o}}={\tfrac {1}{2}}(e_{-}-e_{+})$ as the point at the origin, giving

$n_{\infty }\cdot n_{\text{o}}=-1$.

This procedure has some similarities to the procedure for working with homogeneous coordinates in projective geometry and in this case allows the modeling of Euclidean transformations of $\mathbf {R} ^{3}$ as orthogonal transformations of a subset of $\mathbf {R} ^{4,1}$.

A fast changing and fluid area of GA, CGA is also being investigated for applications to relativistic physics.

Models for projective transformation

Two potential candidates are currently under investigation as the foundation for affine and projective geometry in 3-dimensions ${\mathcal {R}}(3,3)$Template:Sfnand ${\mathcal {R}}(4,4)$ which includes representations for shears and non-uniform scaling, as well as quadric surfaces and conic sections.

A new research model, Quadric Conformal Geometric Algebra (QCGA) ${\mathcal {R}}(9,6)$ is an extension of CGA, dedicated to quadric surfaces. The idea is to represent the objects in low dimensional subspaces of the algebra. QCGA is capable of constructing quadric surfaces either using control points or implicit equations. Moreover, QCGA can compute the intersection of quadric surfaces, as well as, the surface tangent and normal vectors at a point that lies in the quadric surface.

Geometric interpretation

Projection and rejection

In 3-d space, a bivector $a\land b$ defines a 2-d plane subspace (light blue, extends infinitely in indicated directions). Any vector $c$ in 3-d space can be decomposed into its projection $c_{\|}$ onto a plane and its rejection $c_{\perp }$ from this plane.

For any vector $a$ and any invertible vector $m$,

$a=amm^{-1}=(a\cdot m+a\wedge m)m^{-1}=a_{\|m}+a_{\perp m},$

where the projection of $a$ onto $m$ (or the parallel part) is

$a_{\|m}=(a\cdot m)m^{-1}$

and the rejection of $a$ from $m$ (or the orthogonal part) is

$a_{\perp m}=a-a_{\|m}=(a\wedge m)m^{-1}.$

Using the concept of a $k$-blade $B$ as representing a subspace of $V$ and every multivector ultimately being expressed in terms of vectors, this generalizes to projection of a general multivector onto any invertible $k$-blade $B$ as

${\mathcal {P}}_{B}(A)=(A\;\rfloor \;B^{-1})\;\rfloor \;B,$

with the rejection being defined as

${\mathcal {P}}_{B}^{\perp }(A)=A-{\mathcal {P}}_{B}(A).$

The projection and rejection generalize to null blades $B$ by replacing the inverse $B^{-1}$ with the pseudoinverse $B^{+}$ with respect to the contractive product. The outcome of the projection coincides in both cases for non-null blades.Template:SfnTemplate:Sfn For null blades $B$, the definition of the projection given here with the first contraction rather than the second being onto the pseudoinverse should be used, as only then is the result necessarily in the subspace represented by $B$.Template:Sfn The projection generalizes through linearity to general multivectors $A$. The projection is not linear in $B$ and does not generalize to objects $B$ that are not blades.

Reflection

Simple reflections in a hyperplane are readily expressed in the algebra through conjugation with a single vector. These serve to generate the group of general rotoreflections and rotations.

Reflection of vector $c$ along a vector $m$. Only the component of $c$ parallel to $m$ is negated.

The reflection $c'$ of a vector $c$ along a vector $m$, or equivalently in the hyperplane orthogonal to $m$, is the same as negating the component of a vector parallel to $m$. The result of the reflection will be

$c'={-c_{\|m}+c_{\perp m}}={-(c\cdot m)m^{-1}+(c\wedge m)m^{-1}}={(-m\cdot c-m\wedge c)m^{-1}}=-mcm^{-1}$

This is not the most general operation that may be regarded as a reflection when the dimension $n\geq 4$. A general reflection may be expressed as the composite of any odd number of single-axis reflections. Thus, a general reflection $a'$ of a vector $a$ may be written

$a\mapsto a'=-MaM^{-1},$

where

$M=pq\cdots r$ and $M^{-1}=(pq\cdots r)^{-1}=r^{-1}\cdots q^{-1}p^{-1}.$

If we define the reflection along a non-null vector $m$ of the product of vectors as the reflection of every vector in the product along the same vector, we get for any product of an odd number of vectors that, by way of example,

$(abc)'=a'b'c'=(-mam^{-1})(-mbm^{-1})(-mcm^{-1})=-ma(m^{-1}m)b(m^{-1}m)cm^{-1}=-mabcm^{-1}\,$

and for the product of an even number of vectors that

$(abcd)'=a'b'c'd'=(-mam^{-1})(-mbm^{-1})(-mcm^{-1})(-mdm^{-1})=mabcdm^{-1}.$

Using the concept of every multivector ultimately being expressed in terms of vectors, the reflection of a general multivector $A$ using any reflection versor $M$ may be written

$A\mapsto M\alpha (A)M^{-1},$

where $\alpha$ is the automorphism of reflection through the origin of the vector space ($v\mapsto -v$) extended through linearity to the whole algebra.

Rotations

A rotor that rotates vectors in a plane rotates vectors through angle $\theta$, that is $x\mapsto R_{\theta }xR_{\theta }^{\dagger }$ is a rotation of $x$ through angle $\theta$. The angle between $u$ and $v$ is $\theta /2$. Similar interpretations are valid for a general multivector $X$ instead of the vector $x$.

If we have a product of vectors $R=a_{1}a_{2}\cdots a_{r}$ then we denote the reverse as

$R^{\dagger }=(a_{1}a_{2}\cdots a_{r})^{\dagger }=a_{r}\cdots a_{2}a_{1}.$

As an example, assume that $R=ab$ we get

$RR^{\dagger }=abba=ab^{2}a=a^{2}b^{2}=ba^{2}b=baab=R^{\dagger }R.$

Scaling $R$ so that $RR^{\dagger }=1$ then

$(RvR^{\dagger })^{2}=Rv^{2}R^{\dagger }=v^{2}RR^{\dagger }=v^{2}$

so $RvR^{\dagger }$ leaves the length of $v$ unchanged. We can also show that

$(Rv_{1}R^{\dagger })\cdot (Rv_{2}R^{\dagger })=v_{1}\cdot v_{2}$

so the transformation $RvR^{\dagger }$ preserves both length and angle. It therefore can be identified as a rotation or rotoreflection; $R$ is called a rotor if it is a proper rotation (as it is if it can be expressed as a product of an even number of vectors) and is an instance of what is known in GA as a versor.

There is a general method for rotating a vector involving the formation of a multivector of the form $R=e^{-B\theta /2}$ that produces a rotation $\theta$ in the plane and with the orientation defined by a $2$-blade $B$.

Rotors are a generalization of quaternions to $n$-dimensional spaces.

Versor

A $k$-versor is a multivector that can be expressed as the geometric product of $k$ invertible vectors.Template:Sfn Unit quaternions (originally called versors by Hamilton) may be identified with rotors in 3D space in much the same way as real 2D rotors subsume complex numbers; for the details refer to Dorst.Template:Sfn

Some authors use the term “versor product” to refer to the frequently occurring case where an operand is "sandwiched" between operators. The descriptions for rotations and reflections, including their outermorphisms, are examples of such sandwiching. These outermorphisms have a particularly simple algebraic form. Specifically, a mapping of vectors of the form

$V\to V:a\mapsto RaR^{-1}$ extends to the outermorphism ${\mathcal {G}}(V)\to {\mathcal {G}}(V):A\mapsto RAR^{-1}.$

Since both operators and operand are versors there is potential for alternative examples such as rotating a rotor or reflecting a spinor always provided that some geometrical or physical significance can be attached to such operations.

By the Cartan–Dieudonné theorem we have that every isometry can be given as reflections in hyperplanes and since composed reflections provide rotations then we have that orthogonal transformations are versors.

In group terms, for a real, non-degenerate ${\mathcal {G}}(p,q)$, having identified the group ${\mathcal {G}}^{\times }$ as the group of all invertible elements of ${\mathcal {G}}$, Lundholm gives a proof that the "versor group" $\{v_{1}v_{2}\cdots v_{k}\in G:v_{i}\in V^{\times }\}$ (the set of invertible versors) is equal to the Lipschitz group $\Gamma$ (Template:Aka Clifford group, although Lundholm deprecates this usage).Template:Sfn

Subgroups of Template:Math

Lundholm defines the $\operatorname {Pin}$, $\operatorname {Spin}$, and $\operatorname {Spin} ^{+}$ subgroups, generated by unit vectors, and in the case of $\operatorname {Spin}$ and $\operatorname {Spin} ^{+}$, only an even number of such vector factors can be present.Template:Sfn

Subgroup	Definition	Description
$\operatorname {Pin}$	$X\in \Gamma :XX^{\dagger }=\pm 1$	unit versors
$\operatorname {Spin}$	${\operatorname {Pin} }\cap {\mathcal {G}}^{+}$	even unit versors
$\operatorname {Spin} ^{+}$	$X\in \operatorname {Spin} :XX^{\dagger }=1$	rotors

Spinors are defined as elements of the even subalgebra of a real GA; an analysis of the GA approach to spinors is given by Francis and Kosowsky.Template:Sfn

Examples and applications

Hypervolume of a parallelotope spanned by vectors

For vectors $a$ and $b$ spanning a parallelogram we have

$a\wedge b=((a\wedge b)b^{-1})b=a_{\perp b}b$

with the result that $a\wedge b$ is linear in the product of the "altitude" and the "base" of the parallelogram, that is, its area.

Similar interpretations are true for any number of vectors spanning an $n$-dimensional parallelotope; the exterior product of vectors $a_{1},a_{2},\ldots ,a_{n}$, that is $\textstyle \bigwedge _{i=1}^{n}a_{i}$, has a magnitude equal to the volume of the $n$-parallelotope. An $n$-vector does not necessarily have a shape of a parallelotope – this is a convenient visualization. It could be any shape, although the volume equals that of the parallelotope.

Intersection of a line and a plane

A line L defined by points T and P (which we seek) and a plane defined by a bivector B containing points P and Q.

We may define the line parametrically by $p=t+\alpha \ v$ where $p$ and $t$ are position vectors for points P and T and $v$ is the direction vector for the line.

Then

$B\wedge (p-q)=0$ and $B\wedge (t+\alpha v-q)=0$

so

$\alpha ={\frac {B\wedge (q-t)}{B\wedge v}}$

and

$p=t+\left({\frac {B\wedge (q-t)}{B\wedge v}}\right)v.$

Rotating systems

The mathematical description of rotational forces such as torque and angular momentum often makes use of the cross product of vector calculus in three dimensions with a convention of orientation (handedness).

The cross product in relation to the exterior product. In red are the unit normal vector, and the "parallel" unit bivector.

The cross product can be viewed in terms of the exterior product allowing a more natural geometric interpretation of the cross product as a bivector using the dual relationship

$a\times b=-I(a\wedge b).$

For example, torque is generally defined as the magnitude of the perpendicular force component times distance, or work per unit angle.

Suppose a circular path in an arbitrary plane containing orthonormal vectors ${\hat {u}}$ and ${\hat {v}}$ is parameterized by angle.

$\mathbf {r} =r({\hat {u}}\cos \theta +{\hat {v}}\sin \theta )=r{\hat {u}}(\cos \theta +{\hat {u}}{\hat {v}}\sin \theta )$

By designating the unit bivector of this plane as the imaginary number

${i}={\hat {u}}{\hat {v}}={\hat {u}}\wedge {\hat {v}}$

$i^{2}=-1$

this path vector can be conveniently written in complex exponential form

$\mathbf {r} =r{\hat {u}}e^{i\theta }$

and the derivative with respect to angle is

${\frac {d\mathbf {r} }{d\theta }}=r{\hat {u}}ie^{i\theta }=\mathbf {r} i.$

So the torque, the rate of change of work $W$, due to a force $F$, is

$\tau ={\frac {dW}{d\theta }}=F\cdot {\frac {dr}{d\theta }}=F\cdot (\mathbf {r} i).$

Unlike the cross product description of torque, $\tau =\mathbf {r} \times F$, the geometric algebra description does not introduce a vector in the normal direction; a vector that does not exist in two and that is not unique in greater than three dimensions. The unit bivector describes the plane and the orientation of the rotation, and the sense of the rotation is relative to the angle between the vectors ${\hat {u}}$ and ${\hat {v}}$.

Geometric calculus

Geometric calculus extends the formalism to include differentiation and integration including differential geometry and differential forms.Template:Sfn

Essentially, the vector derivative is defined so that the GA version of Green's theorem is true,

$\int _{A}dA\,\nabla f=\oint _{\partial A}dx\,f$

and then one can write

$\nabla f=\nabla \cdot f+\nabla \wedge f$

as a geometric product, effectively generalizing Stokes' theorem (including the differential form version of it).

In $1D$ when $A$ is a curve with endpoints $a$ and $b$, then

$\int _{A}dA\,\nabla f=\oint _{\partial A}dx\,f$

reduces to

$\int _{a}^{b}dx\,\nabla f=\int _{a}^{b}dx\cdot \nabla f=\int _{a}^{b}df=f(b)-f(a)$

or the fundamental theorem of integral calculus.

Also developed are the concept of vector manifold and geometric integration theory (which generalizes differential forms).

History

Before the 20th century

Although the connection of geometry with algebra dates as far back at least to Euclid's Elements in the third century B.C. (see Greek geometric algebra), GA in the sense used in this article was not developed until 1844, when it was used in a systematic way to describe the geometrical properties and transformations of a space. In that year, Hermann Grassmann introduced the idea of a geometrical algebra in full generality as a certain calculus (analogous to the propositional calculus) that encoded all of the geometrical information of a space.Template:Sfn Grassmann's algebraic system could be applied to a number of different kinds of spaces, the chief among them being Euclidean space, affine space, and projective space. Following Grassmann, in 1878 William Kingdon Clifford examined Grassmann's algebraic system alongside the quaternions of William Rowan Hamilton in Template:Harvard citation. From his point of view, the quaternions described certain transformations (which he called rotors), whereas Grassmann's algebra described certain properties (or Strecken such as length, area, and volume). His contribution was to define a new product — the geometric product – on an existing Grassmann algebra, which realized the quaternions as living within that algebra. Subsequently, Rudolf Lipschitz in 1886 generalized Clifford's interpretation of the quaternions and applied them to the geometry of rotations in $n$ dimensions. Later these developments would lead other 20th-century mathematicians to formalize and explore the properties of the Clifford algebra.

Nevertheless, another revolutionary development of the 19th-century would completely overshadow the geometric algebras: that of vector analysis, developed independently by Josiah Willard Gibbs and Oliver Heaviside. Vector analysis was motivated by James Clerk Maxwell's studies of electromagnetism, and specifically the need to express and manipulate conveniently certain differential equations. Vector analysis had a certain intuitive appeal compared to the rigors of the new algebras. Physicists and mathematicians alike readily adopted it as their geometrical toolkit of choice, particularly following the influential 1901 textbook Vector Analysis by Edwin Bidwell Wilson, following lectures of Gibbs.

In more detail, there have been three approaches to geometric algebra: quaternionic analysis, initiated by Hamilton in 1843 and geometrized as rotors by Clifford in 1878; geometric algebra, initiated by Grassmann in 1844; and vector analysis, developed out of quaternionic analysis in the late 19th century by Gibbs and Heaviside. The legacy of quaternionic analysis in vector analysis can be seen in the use of $i$, $j$, $k$ to indicate the basis vectors of $\mathbf {R} ^{3}$: it is being thought of as the purely imaginary quaternions. From the perspective of geometric algebra, the even subalgebra of the Space Time Algebra is isomorphic to the GA of 3D Euclidean space and quaternions are isomorphic to the even subalgebra of the GA of 3D Euclidean space, which unifies the three approaches.

20th century and present

Progress on the study of Clifford algebras quietly advanced through the twentieth century, although largely due to the work of abstract algebraists such as Hermann Weyl and Claude Chevalley. The geometrical approach to geometric algebras has seen a number of 20th-century revivals. In mathematics, Emil Artin's Geometric AlgebraTemplate:Sfn discusses the algebra associated with each of a number of geometries, including affine geometry, projective geometry, symplectic geometry, and orthogonal geometry. In physics, geometric algebras have been revived as a "new" way to do classical mechanics and electromagnetism, together with more advanced topics such as quantum mechanics and gauge theory.Template:Sfn David Hestenes reinterpreted the Pauli and Dirac matrices as vectors in ordinary space and spacetime, respectively, and has been a primary contemporary advocate for the use of geometric algebra.

In computer graphics and robotics, geometric algebras have been revived in order to efficiently represent rotations and other transformations. For applications of GA in robotics (screw theory, kinematics and dynamics using versors), computer vision, control and neural computing (geometric learning) see Bayro (2010).

Software

GA is a very application-oriented subject. There is a reasonably steep initial learning curve associated with it, but this can be eased somewhat by the use of applicable software. The following is a list of freely available software that does not require ownership of commercial software or purchase of any commercial products for this purpose:

Actively developed open source projects

• clifford - Numeric Geometric Algebra Module for Python.
• galgebra - Symbolic Geometric Algebra Module for Python by Alan Bromborsky (uses sympy).
• GATL - A template C++ library that uses the lazy evaluation strategy to automatically execute low-level algebraic manipulations in compile time in order to produce more efficient programs.
• ganja.js - Geometric Algebra for Javascript (with operator overloading and algebraic literals)
• klein - Production-oriented SSE-optimized C++ library, specializing in 3D Projective Geometric Algebra ($\mathbf {P} (\mathbb {R} _{3,0,1}^{*})$)
• Versor, A lightweight templated C++ Library with an OpenGL interface for efficient geometric algebra programming in arbitrary metrics, including conformal
• Grassmann.jl - Conformal geometric product algebra based on static dual multivectors with graded-blade indexing (written in Julia language)

Other projects

• GA Viewer Fontijne, Dorst, Bouma & Mann
• GAwxM GitHub - GA using wxMaxima, Open Source software using a free Computer Algebra System, includes readme files for motivation and setup.
• CLUViz Perwass

Software allowing script creation and including sample visualizations, manual and GA introduction.

• Gaigen Fontijne

For programmers, this is a code generator with support for C, C++, C# and Java.

• Cinderella Visualizations Hitzer and Dorst.
• Gaalop [1] Standalone GUI-Application that uses the Open-Source Computer Algebra Software Maxima to break down CLUViz code into C/C++ or Java code.
• Gaalop Precompiler [2] Precompiler based on Gaalop integrated with CMake.
• Gaalet, C++ Expression Template Library Seybold.
• Clifford Algebra with Mathematica clifford.m
• Clifford Algebra with GiNaC built-in classes

Benchmark project

• ga-benchmark - A benchmark for C/C++ Geometric Algebra libraries and library generators. The latest results of the ga-benchmark can be found here.

Voci correlate

• Algebra vettoriale e algebra geometrica
• Algebra di Clifford
• Algebra di Grassmann–Cayley
• Algebra spaziotempo
• Spinore
• Quaternione
• Algebra dello spazio fisico
• Algebra geometrica universale

Note

Riferimenti a libri, riviste...

Postille o brevi osservazioni

Bibliografia

Arranged chronogically

• Hermann Grassmann, Die lineale Ausdehnungslehre ein neuer Zweig der Mathematik: dargestellt und durch Anwendungen auf die übrigen Zweige der Mathematik, wie auch auf die Statik, Mechanik, die Lehre vom Magnetismus und die Krystallonomie erläutert, O. Wigand, 1844.
• Emil Artin, Geometric algebra, Wiley, 1988, DOI:10.1002/9781118164518.
• David Hestenes, Space–time Algebra, Gordon and Breach, 1966.
• Gravitation, W.H. Freeman, 1973.
• Nicolas Bourbaki, Eléments de Mathématique. Algèbre, Hermann, 1980.
• Clifford Algebra to Geometric Calculus, a Unified Language for Mathematics and Physics, Springer Netherlands, 1984, ISBN 9789027716736.
• A Unified Language for Mathematics and Physics, in Clifford Algebras and Their Applications in Mathematical Physics, vol. 183, Springer, 1986, pp. 1–23, DOI:10.1007/978-94-009-4728-3_1.
• Chris J. L. Doran, Geometric Algebra and its Application to Mathematical Physics, University of Cambridge, 1994, DOI:10.17863/CAM.16148.
• Clifford (Geometric) Algebra with Applications to Physics, Mathematics, and Engineering, Birkhäuser, 2011.
• Clifford Algebras and Geometric Algebra, in Advances in Applied Clifford Algebras, vol. 7, n. 2, 1997, pp. 91–102, DOI:10.1007/BF03041220.
• David Hestenes, New Foundations for Classical Mechanics, Springer Verlag, 1999.
• Joan Lasenby, A Unified Mathematical Language for Physics and Engineering in the 21st Century (PDF), in Philosophical Transactions of the Royal Society A, vol. 358, n. 1765, 2000, pp. 21–39, DOI:10.1098/rsta.2000.0517.
• W. E. Baylis, Electrodynamics: A Modern Geometric Approach, Birkhäuser, 2002.
• Leo Dorst, Applications of Geometric Algebra in Computer Science and Engineering, Birkhäuser, 2002, pp. 35–46, DOI:10.1007/978-1-4612-0089-5_2.
• Chris J. L. Doran, Geometric Algebra for Physicists (PDF), Cambridge University Press, 2003.
• David Hestenes, Oersted Medal Lecture 2002: Reforming the Mathematical Language of Physics (PDF), in Am. J. Phys., vol. 71, n. 2, 2003, pp. 104–121, DOI:10.1119/1.1522700.
• Dietmar Hildenbrand, Geometric Algebra and its Application to Computer Graphics (PDF), in Proceedings of Eurographics 2004, 2004, DOI:10.2312/egt.20041032.
• J. Bain, The ontology of spacetime, Elsevier, 2006.
• Leo Dorst, Geometric algebra for computer science: an object-oriented approach to geometry, Elsevier, 2007.
• Roger Penrose, The Road to Reality, Vintage books, 2007.
• Matthew R. Francis, The Construction of Spinors in Geometric Algebra, in Annals of Physics, vol. 317, n. 2, 2008, pp. 383–409, DOI:10.1016/j.aop.2004.11.008.
• Hongbo Li, Invariant Algebras and Geometric Reasoning, World Scientific, 2008.. Chapter 1 as PDF
• John A. Vince, Geometric Algebra for Computer Graphics, Springer, 2008.
• Template:Cite arxiv
• Christian Perwass, Geometric Algebra with Applications in Engineering, vol. 4, Springer Science & Business Media, 2009, DOI:10.1007/978-3-540-89068-3.
• Eduardo Bayro-Corrochano, Geometric Computing for Wavelet Transforms, Robot Vision, Learning, Control and Action, Springer Verlag, 2010.
• Geometric Algebra Computing in Engineering and Computer Science, Springer, 2010. Extract online at http://geocalc.clas.asu.edu/html/UAFCG.html #5 New Tools for Computational Geometry and rejuvenation of Screw Theory
• Ron Goldman, Rethinking Quaternions: Theory and Computation, Morgan & Claypool, 2010.
• Guide to Geometric Algebra in Practice, Springer, 2011.
• Alan Macdonald, Linear and Geometric Algebra, CreateSpace, 2011.
• John Snygg, A New Approach to Differential Geometry using Clifford's Geometric Algebra, Springer, 2011.
• Dietmar Hildenbrand, Foundations of Geometric Algebra Computing, vol. 1479, n. 1, 2013, pp. 27–30, DOI:10.1063/1.4756054.
• Alan Bromborsky, An introduction to Geometric Algebra and Calculus (PDF), 2014.
• Daniel Klawitter, Clifford Algebras: Geometric Modelling and Chain Geometries with Application in Kinematics, Springer, 2014.
• Kenichi Kanatani, Understanding Geometric Algebra: Hamilton, Grassmann, and Clifford for Computer Vision and Graphics, CRC Press, 2015.
• Template:Cite arxiv
• David Hestenes, The Genesis of Geometric Algebra:A Personal Retrospective, in Advances in Applied Clifford Algebra, vol. 27, n. 1, 11 April 2016, pp. 351–379, DOI:10.1007/s00006-016-0664-z.
• Leo Dorst, 3D Oriented Projective Geometry Through Versors of $\mathbf {R} ^{3,3}$, Springer, 2016.
• Jayme Vaz, An Introduction to Clifford Algebras and Spinors, 2016.
• Template:Cite arxiv
• Eduardo Bayro-Corrochano, Computer Vision, Graphics and Neurocomputing, Springer, 2018, ISBN 978-3-319-74830-6.
• Carlile Lavor, A Geometric Algebra Invitation to Space-Time Physics, Robotics and Molecular Geometry, Springer, 2018, pp. 1–, ISBN 978-3-319-90665-2.

Collegamenti esterni

• A Survey of Geometric Algebra and Geometric Calculus Alan Macdonald, Luther College, Iowa.
• Imaginary Numbers are not Real – the Geometric Algebra of Spacetime. Introduction (Cambridge GA group).
• Geometric Algebra 2015, Masters Course in Scientific Computing, from Dr. Chris Doran (Cambridge).
• Maths for (Games) Programmers: 5 – Multivector methods. Comprehensive introduction and reference for programmers, from Ian Bell.
• IMPA Summer School 2010 Fernandes Oliveira Intro and Slides.
• University of Fukui E.S.M. Hitzer and Japan GA publications.
• Google Group for GA
• Geometric Algebra Primer Introduction to GA, Jaap Suter.
• Geometric Algebra Resources curated wiki, Pablo Bleyer.
• Applied Geometric Algebras in Computer Science and Engineering 2018 Early Proceedings
• GAME2020Geometric Algebra Mini Event

'Traduzioni inglesi di libri e riviste passate

• G. Combebiac, "calculus of tri-quaternions" (Doctoral dissertation)
• M. Markic, "Transformants: A new mathematical vehicle. A synthesis of Combebiac's tri-quaternions and Grassmann's geometric system. The calculus of quadri-quaternions"
• C. Burali-Forti, "The Grassmann method in projective geometry" A compilation of three notes on the application of exterior algebra to projective geometry
• C. Burali-Forti, "Introduction to Differential Geometry, following the method of H. Grassmann" Early book on the application of Grassmann algebra
• H. Grassmann, "Mechanics, according to the principles of the theory of extension" One of his papers on the applications of exterior algebra.

Gruppi di ricerca

• Geometric Calculus International. Links to Research groups, Software, and Conferences, worldwide.
• Cambridge Geometric Algebra group. Full-text online publications, and other material.
• University of Amsterdam group
• Geometric Calculus research & development (Arizona State University).
• GA-Net blog and newsletter archive. Geometric Algebra/Clifford Algebra development news.
• Geometric Algebra for Perception Action Systems. Geometric Cybernetics Group (CINVESTAV, Campus Guadalajara, Mexico).