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  2. Convex combination - Wikipedia

    en.wikipedia.org/wiki/Convex_combination

    A conical combination is a linear combination with nonnegative coefficients. When a point is to be used as the reference origin for defining displacement vectors, then is a convex combination of points ,, …, if and only if the zero displacement is a non-trivial conical combination of their respective displacement vectors relative to .

  3. Linear combination - Wikipedia

    en.wikipedia.org/wiki/Linear_combination

    Consider the vectors (polynomials) p 1 := 1, p 2 := x + 1, and p 3 := x 2 + x + 1. Is the polynomial x 21 a linear combination of p 1, p 2, and p 3? To find out, consider an arbitrary linear combination of these vectors and try to see when it equals the desired vector x 21. Picking arbitrary coefficients a 1, a 2, and a 3, we want

  4. Triple product - Wikipedia

    en.wikipedia.org/wiki/Triple_product

    In geometry and algebra, the triple product is a product of three 3-dimensional vectors, usually Euclidean vectors.The name "triple product" is used for two different products, the scalar-valued scalar triple product and, less often, the vector-valued vector triple product.

  5. Resultant - Wikipedia

    en.wikipedia.org/wiki/Resultant

    Given two homogeneous polynomials P(x, y) and Q(x, y) of respective total degrees p and q, their homogeneous resultant is the determinant of the matrix over the monomial basis of the linear map (,) +, where A runs over the bivariate homogeneous polynomials of degree q − 1, and B runs over the homogeneous polynomials of degree p − 1. In ...

  6. Affine combination - Wikipedia

    en.wikipedia.org/wiki/Affine_combination

    The elements x 1, ..., x n can also be points of a Euclidean space, and, more generally, of an affine space over a field K. In this case the α i {\displaystyle \alpha _{i}} are elements of K (or R {\displaystyle \mathbb {R} } for a Euclidean space), and the affine combination is also a point.

  7. 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.

  8. Vector calculus - Wikipedia

    en.wikipedia.org/wiki/Vector_calculus

    Vector calculus or vector analysis is a branch of mathematics concerned with the differentiation and integration of vector fields, primarily in three-dimensional Euclidean space, . [1] The term vector calculus is sometimes used as a synonym for the broader subject of multivariable calculus, which spans vector calculus as well as partial differentiation and multiple integration.

  9. Linear subspace - Wikipedia

    en.wikipedia.org/wiki/Linear_subspace

    However, there are exceptions to this rule. For example, the subspace of K 3 spanned by the three vectors (1, 0, 0), (0, 0, 1), and (2, 0, 3) is just the xz-plane, with each point on the plane described by infinitely many different values of t 1, t 2, t 3. In general, vectors v 1, ... , v k are called linearly independent if