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The complex dot product leads to the notions of Hermitian forms and general inner product spaces, which are widely used in mathematics and physics. The self dot product of a complex vector =, involving the conjugate transpose of a row vector, is also known as the norm squared, = ‖ ‖, after the Euclidean norm; it is a vector generalization ...
Also, the dot, cross, and dyadic products can all be expressed in matrix form. Dyadic expressions may closely resemble the matrix equivalents. The dot product of a dyadic with a vector gives another vector, and taking the dot product of this result gives a scalar derived from the dyadic.
The following are important identities in vector algebra.Identities that only involve the magnitude of a vector ‖ ‖ and the dot product (scalar product) of two vectors A·B, apply to vectors in any dimension, while identities that use the cross product (vector product) A×B only apply in three dimensions, since the cross product is only defined there.
The outer product of tensors is also referred to as their tensor product, and can be used to define the tensor algebra. The outer product contrasts with: The dot product (a special case of "inner product"), which takes a pair of coordinate vectors as input and produces a scalar
Inner product spaces generalize Euclidean vector spaces, in which the inner product is the dot product or scalar product of Cartesian coordinates. Inner product spaces of infinite dimension are widely used in functional analysis. Inner product spaces over the field of complex numbers are sometimes referred to as unitary spaces.
The cross product occurs frequently in the study of rotation, where it is used to calculate torque and angular momentum. It can also be used to calculate the Lorentz force exerted on a charged particle moving in a magnetic field. The dot product is used to determine the work done by a constant force.
(This can be derived by noting that we want to get the correct answer for the dot product operation when multiplying by an arbitrary vector , with components [...]). The covariance of these covector components is then seen by noting that if a transformation described by an n × n {\displaystyle n\times n} invertible matrix M were to be applied ...
The dot products on every tangent plane, packaged together into one mathematical object, are a Riemannian metric. In differential geometry , a Riemannian manifold is a geometric space on which many geometric notions such as distance, angles, length, volume, and curvature are defined.