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  2. Dot product - Wikipedia

    en.wikipedia.org/wiki/Dot_product

    When vectors are represented by column vectors, the dot product can be expressed as a matrix product involving a conjugate transpose, denoted with the superscript H: =. In the case of vectors with real components, this definition is the same as in the real case.

  3. Vector algebra relations - Wikipedia

    en.wikipedia.org/wiki/Vector_algebra_relations

    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.

  4. Triple product - Wikipedia

    en.wikipedia.org/wiki/Triple_product

    The dot product of two vectors is a scalar but the dot product of a pseudovector and a vector is a pseudoscalar, so the scalar triple product (of vectors) must be pseudoscalar-valued. If T is a proper rotation then

  5. Lists of vector identities - Wikipedia

    en.wikipedia.org/wiki/Lists_of_vector_identities

    There are two lists of mathematical identities related to vectors: Vector algebra relations — regarding operations on individual vectors such as dot product, cross product, etc. Vector calculus identities — regarding operations on vector fields such as divergence, gradient, curl, etc.

  6. Vector multiplication - Wikipedia

    en.wikipedia.org/wiki/Vector_multiplication

    The dot product of two vectors can be defined as the product of the magnitudes of the two vectors and the cosine of the angle between the two vectors. Alternatively, it is defined as the product of the projection of the first vector onto the second vector and the magnitude of the second vector.

  7. Cosine similarity - Wikipedia

    en.wikipedia.org/wiki/Cosine_similarity

    In data analysis, cosine similarity is a measure of similarity between two non-zero vectors defined in an inner product space. Cosine similarity is the cosine of the angle between the vectors; that is, it is the dot product of the vectors divided by the product of their lengths. It follows that the cosine similarity does not depend on the ...

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  9. Covariance and contravariance of vectors - Wikipedia

    en.wikipedia.org/wiki/Covariance_and_contra...

    The dot product operator involving vectors is a good example of a covector. To illustrate, assume we have a covector defined as v ⋅ {\displaystyle \mathbf {v} \ \cdot } , where v {\displaystyle \mathbf {v} } is a vector.