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  2. Dot product - Wikipedia

    en.wikipedia.org/wiki/Dot_product

    Illustration showing how to find the angle between vectors using the dot product Calculating bond angles of a symmetrical tetrahedral molecular geometry using a dot product. In Euclidean space, a Euclidean vector is a geometric object that possesses both a magnitude and a direction. A vector can be pictured as an arrow.

  3. Cosine similarity - Wikipedia

    en.wikipedia.org/wiki/Cosine_similarity

    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 magnitudes of the vectors, but only on their angle. The cosine similarity always belongs to the interval [,].

  4. Vector projection - Wikipedia

    en.wikipedia.org/wiki/Vector_projection

    The scalar projection is defined as [2] = ‖ ‖ ⁡ = ^ where the operator ⋅ denotes a dot product, ‖a‖ is the length of a, and θ is the angle between a and b. The scalar projection is equal in absolute value to the length of the vector projection, with a minus sign if the direction of the projection is opposite to the direction of b ...

  5. Tetrahedral molecular geometry - Wikipedia

    en.wikipedia.org/wiki/Tetrahedral_molecular_geometry

    This angle may be calculated from the dot product of the two vectors, defined as a ⋅ b = ‖ a ‖ ‖ b ‖ cos θ where ‖ a ‖ denotes the length of vector a. As shown in the diagram, the dot product here is –1 and the length of each vector is √ 3, so that cos θ = – ⁠ 1 / 3 ⁠ and the tetrahedral bond angle θ = arccos ...

  6. 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.

  7. Inner product space - Wikipedia

    en.wikipedia.org/wiki/Inner_product_space

    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.

  8. Direction cosine - Wikipedia

    en.wikipedia.org/wiki/Direction_cosine

    If vectors u and v have direction cosines (α u, β u, γ u) and (α v, β v, γ v) respectively, with an angle θ between them, their units vectors are ^ = + + (+ +) = + + ^ = + + (+ +) = + +. Taking the dot product of these two unit vectors yield, ^ ^ = + + = ⁡, where θ is the angle between the two unit vectors, and is also the angle between u and v.

  9. Lists of vector identities - Wikipedia

    en.wikipedia.org/wiki/Lists_of_vector_identities

    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.