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A two-vector or bivector [1] is a tensor of type () and it is the dual of a two-form, meaning that it is a linear functional which maps two-forms to the real numbers (or more generally, to scalars). The tensor product of a pair of vectors is a two-vector. Then, any two-form can be expressed as a linear combination of tensor products of pairs of ...
It forms a loop in the first quadrant with a double point at the origin and asymptote + + =. It is symmetrical about the line y = x {\displaystyle y=x} . As such, the two intersect at the origin and at the point ( 3 a / 2 , 3 a / 2 ) {\displaystyle (3a/2,3a/2)} .
The cross product of two vectors u and v would be represented as: By some conventions (e.g. in France and in some areas of higher mathematics), this is also denoted by a wedge, [ 13 ] which avoids confusion with the wedge product since the two are functionally equivalent in three dimensions: u ∧ v {\displaystyle \mathbf {u} \wedge \mathbf {v} }
In fact, every x ∈ H can then be written uniquely as x = v + w, with v ∈ V and w ∈ V ⊥. Therefore, H is the internal Hilbert direct sum of V and V ⊥ . The linear operator P V : H → H that maps x to v is called the orthogonal projection onto V .
As the name implies, the divergence is a (local) measure of the degree to which vectors in the field diverge. The divergence of a tensor field T {\displaystyle \mathbf {T} } of non-zero order k is written as div ( T ) = ∇ ⋅ T {\displaystyle \operatorname {div} (\mathbf {T} )=\nabla \cdot \mathbf {T} } , a contraction of a tensor field ...
If imagined as a parallelogram, with the origin for the vectors at 0, then signed area is the determinant of the vectors' Cartesian coordinates (a x b y − b x a y). [21] The cross product a × b is orthogonal to the bivector a ∧ b. In three dimensions all bivectors can be generated by the exterior product of two vectors.
The transformed data matrix Y is obtained from the original matrix X by centering and optionally standardizing the columns (the variables). Using the SVD, we can write Y = Σ k =1,... p d k u k v k T ;, where the u k are n -dimensional column vectors, the v k are p -dimensional column vectors, and the d k are a non-increasing sequence of non ...
The tensor product of V and its dual space is isomorphic to the space of linear maps from V to V: a dyadic tensor vf is simply the linear map sending any w in V to f(w)v. When V is Euclidean n-space, we can use the inner product to identify the dual space with V itself, making a dyadic tensor an elementary tensor product of two vectors in ...