Outer product in geometric algebra

In summary, the author is discussing a rule for calculating geometric products in an n-dimensional space. The rule is that the geometric product is just the cross product. However, for 3 vectors, the product is not true in Euclidian space.
  • #1
Silviu
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Hello! I am reading so very introductory stuff on geometric algebra and at a point the author says that, as a rule for calculation geometric products, we have that ##e_{12..n}=e_1\wedge e_2 \wedge ...\wedge e_n = e_1e_2...e_n##, with ##e_i## the orthonormal basis of an n-dimensional space, and I am not sure I understand this. As far as I understood, the wedge product of 2 vectors, is just the cross product and we have ##e_1e_2=e_1 \cdot e_2 + e_1 \wedge e_2=e_1 \wedge e_2##, which makes sense as the dot product of 2 perpendicular vectors is 0. But for 3 vectors we would have ##e_1e_2e_3=(e_1 \wedge e_2)e_3=(e_1 \wedge e_2) \cdot e_3 + (e_1 \wedge e_2)\wedge e_3 = e_1 \wedge e_2 \wedge e_3##, which is not true in euclidian space. So can someone explain to me how does the rule the author mentioned, works? Thank you!
 
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  • #2
(e1∧e2) is not the cross product. It is not a vector in the direction of e3. It is an oriented element of area in the plane of e1 and e2. The vector e3 is orthogonal to that plane. So (e1∧e2)⋅e3 = 0. Visualizing (e1∧e2) as the cross product vector is a habit that will cause trouble.
 
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  • #3
FactChecker said:
(e1∧e2) is not the cross product. It is not a vector in the direction of e3. It is an oriented element of area in the plane of e1 and e2. The vector e3 is orthogonal to that plane. So (e1∧e2)⋅e3 = 0.
Ok, this makes more sense. But what is the direction of ##e_1 \wedge e_2##, if it is not perpendicular to the plane?
 
  • #4
It is not a vector with a direction. It is an area element in the e1, e2 plane with a clockwise or counter-clockwise orientation.

EDIT: More precisely, e1∧e2 is not in the familiar R3 vector space with basis e1, e2, and e3. It is an oriented area vector in the vector space of oriented areas with basis e1∧e2, e2∧e3, and e3∧e1.
 
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  • #5
Silviu said:
Ok, this makes more sense. But what is the direction of ##e_1 \wedge e_2##, if it is not perpendicular to the plane?
A smooth function ##f## is a vector, as it can be viewed as an element of the vector space ##\mathcal{C}^\infty(\mathbb{R})##. In which direction does ##f## point? With ##v \wedge w## it is similar. It is a vector as an element of a Graßmann algebra, but without a basis, no coordinates and without coordinates no direction. If ##\{u,v,w\}## are linear independent vectors in, say ##\mathbb{R}^3##, then ##\{1,u,v,w,u\wedge v, v\wedge w, w\wedge u, u\wedge v \wedge w\}## are also linear independent in ##\bigoplus_n \bigwedge^n(\mathbb{R}^3)##.
 
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  • #6
FactChecker said:
(e1∧e2) is not the cross product. It is not a vector in the direction of e3. It is an oriented element of area in the plane of e1 and e2. The vector e3 is orthogonal to that plane. So (e1∧e2)⋅e3 = 0. Visualizing (e1∧e2) as the cross product vector is a habit that will cause trouble.
Is there a formal proof of (e1∧e2)⋅e3 = 0 ? Thanks
 
  • #7
hjaramil said:
Is there a formal proof of (e1∧e2)⋅e3 = 0 ? Thanks
The Graßmann algebra ##\bigwedge^m( V)## normally doesn't have an inner product. However, if ##V## has, as in the case of a real vector space, there can be defined one. So your question comes down to: "How is an inner product on a Graßmann algebra defined, if the vector space has one?", i.e. how to interpret the dot in the equation above. The definition goes
$$
\langle (u_1\wedge \ldots \wedge u_m)\; , \;(v_1\wedge \ldots \wedge v_m)\rangle = \det \begin{bmatrix}\langle u_1,v_1\rangle&\ldots& \langle u_1,v_m\rangle \\ \vdots & \ldots &\vdots \\ \langle u_m,v_1\rangle&\ldots& \langle u_m,v_m\rangle \end{bmatrix}
$$
which shows that only products of equal grade are defined. To expand this on the entire Graßmann algebra, subspaces of different grade are defined to be orthongonal. It is in accordance to the definition, because such a determinant would also be zero.
 
  • #8
Silviu said:
Hello! I am reading so very introductory stuff on geometric algebra and at a point the author says that, as a rule for calculation geometric products, we have that ##e_{12..n}=e_1\wedge e_2 \wedge ...\wedge e_n = e_1e_2...e_n##, with ##e_i## the orthonormal basis of an n-dimensional space, and I am not sure I understand this. As far as I understood, the wedge product of 2 vectors, is just the cross product and we have ##e_1e_2=e_1 \cdot e_2 + e_1 \wedge e_2=e_1 \wedge e_2##, which makes sense as the dot product of 2 perpendicular vectors is 0. But for 3 vectors we would have ##e_1e_2e_3=(e_1 \wedge e_2)e_3=(e_1 \wedge e_2) \cdot e_3 + (e_1 \wedge e_2)\wedge e_3 = e_1 \wedge e_2 \wedge e_3##, which is not true in euclidian space. So can someone explain to me how does the rule the author mentioned, works? Thank you!

It would be very helpful if you mention who the author is and which book you are reading. It helps in understanding the context.
 
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  • #9
steenis said:
It would be very helpful if you mention who the author is and which book you are reading. It helps in understanding the context.
I believe I answered my own question. There are several definitions of inner products. Here is one.
fresh_42 said:
The Graßmann algebra ##\bigwedge^m( V)## normally doesn't have an inner product. However, if ##V## has, as in the case of a real vector space, there can be defined one. So your question comes down to: "How is an inner product on a Graßmann algebra defined, if the vector space has one?", i.e. how to interpret the dot in the equation above. The definition goes
$$
\langle (u_1\wedge \ldots \wedge u_m)\; , \;(v_1\wedge \ldots \wedge v_m)\rangle = \det \begin{bmatrix}\langle u_1,v_1\rangle&\ldots& \langle u_1,v_m\rangle \\ \vdots & \ldots &\vdots \\ \langle u_m,v_1\rangle&\ldots& \langle u_m,v_m\rangle \end{bmatrix}
$$
which shows that only products of equal grade are defined. To expand this on the entire Graßmann algebra, subspaces of different grade are defined to be orthongonal. It is in accordance to the definition, because such a determinant would also be zero.
I believe I answered my own question. There are several definitions of inner prodcut. Here is one (right contraction) if $$A_j$$ is a j-vector and $$B_k$$ is a k-vector then
$$A_j \cdot B_k= \langle AB \rangle_{j-k}$$. Then $$(e_1 \wedge e_2) \cdot e_3 = \langle e_1 e_2 e_3 \rangle_{2-1} = 0$$.
 

1. What is the outer product in geometric algebra?

The outer product in geometric algebra is a mathematical operation that takes two vectors and produces a multivector. It is also known as the wedge product or exterior product, and is denoted by the symbol ∧. It is used to describe geometric properties such as area, volume, and orientation.

2. How is the outer product different from the inner product?

The outer product is a multivector while the inner product is a scalar. The outer product produces a result that is perpendicular to both input vectors, while the inner product produces a result that is parallel to one of the input vectors. Furthermore, the outer product is anti-commutative, meaning that changing the order of the input vectors results in a negative sign, while the inner product is commutative.

3. What are the applications of the outer product in geometric algebra?

The outer product has various applications in physics, computer graphics, and robotics. It is used to describe geometric transformations, such as rotations and reflections, in a compact and intuitive way. It is also used in computer vision for 3D reconstruction and in robotics for kinematics and dynamics calculations.

4. Can the outer product be extended to higher dimensions?

Yes, the outer product can be extended to any number of dimensions. In geometric algebra, the outer product of n vectors produces an n-dimensional multivector. This is useful for describing geometric properties in higher-dimensional spaces, such as hypersurfaces or tensors.

5. How is the outer product related to the cross product?

The outer product is a generalization of the cross product in 3D. In geometric algebra, the cross product can be written as the outer product of two vectors, which produces a bivector (2D multivector) perpendicular to the input vectors. However, the outer product can be applied to any number of dimensions, while the cross product is limited to 3D. Additionally, the outer product is more general and can represent higher-dimensional analogues of the cross product, such as the 7D cross product.

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