|  Vectors and Vector Spaces | 
In General > s.a. vector calculus; vector field.
  * Versions: An arrow between points
    on \(\mathbb R\)n; An infinitesimal
    displacement in a manifold; An element of a vector space; A contravariant rank-1 tensor,
    i.e., one of type (1, 0).
  * In physics: Example of the types
    of vectors used in physics are Gibbs' three-vectors, Minkowski four-vectors, complex
    spinors in quantum mechanics, quaternions used to describe rigid body rotations and
    vectors defined in Clifford geometric algebra.
  @ References: Weinreich 98 [geometrical];
    Lesche et al AJP(92)jun [dual/covector];
    Fleisch 11 [II];
    Chappel et al IEEE(16)-a1509 [different vector formalisms, historical perspective].
  @ Covariant and contravariant: Schmidt AJP(97)nov-gq;
    Kumar a1002 [pedagogical].
Vector Space
  > s.a. Bilinear Form; Flag;
  Hahn-Banach Theorem; Subspace.
  $ Def: A set X with two
    binary operations +: X × X → X and 
    · : K × X → X, where K is
    a field (usually \(\mathbb R\) or \(\mathbb C\)), satisfying some properties.
  * Linearly independent vectors: A set
    of two or more vectors in a vector space is linearly independent if the only linear
    combination of the vectors in the set that equals 0 is the one with all coefficients
    equal to zero; Alternatively, none of the vectors is a linear combination of the others.
  * New vector spaces out of old:
    > see Direct Sum (of R-modules),
    direct or tensor product.
  * Ordered vector space: A vector space
    with a partial order < satisfying simple compatibility conditions with the addition
    and scalar multiplication.
  * Relationships: A vector space structure is
    stronger than an affine structure; > s.a. affine structure.
  @ References: Halmos 74.
  > Online resources:
    see MathWorld page;
    Wikipedia page.
Topological Vector Space > s.a. Banach Space;
  Fréchet Space; norm.
  $ Def: A vector space X over
    a topological field K such that the two operations are continuous.
  * Examples: Any metric
    space or normed vector space, including Banach spaces and Hilbert spaces.
  * Remark: On a real or complex
    vector space there is a unique, natural topology that makes it a topological vector
    space (by the Tychonoff theorem).
  * Locally convex: A tvs which admits
    a topological base of convex sets; A real vector space with a (Hausdorff) topology
    generated by a family of seminorms.
  @ References: Bourbaki 66;
    Kelley 76.
  > Online resources:
    see MathWorld page;
    Wikipedia page.
Vector Algebra in Euclidean Space
  * Scalar (dot) product: X · Y
    = XY cosθ = ∑i
    XiYi.
  * Vector (cross) product:
    (Xx, Xy,
    Xz) ×
    (Yx, Yy,
    Yz)
    = (XyYz−XzYy,
    XzYx−XxYz,
  XxYy−XyYx),
    which can also be expressed in determinant form, or as (X ×
    Y)i
    = εijk
    XjYk.
  @ References: Silagadze JPA(02) [7D generalization of vector product].
Inner Product > s.a. distance;
  formulations of quantum mechanics; hilbert space;
  Schwarz Inequality; types of symplectic structures.
  $ Def: A map \(\langle\ ,\  \rangle:
    V\times V\to {\mathbb R}\ ({\mathbb C})\), where V is a vector space
    over \(\mathbb R\) (\(\mathbb C\)), which is bilinear (sesquilinear), Hermitian,
    and positive definite.
  * Examples: For probability
    distributions, the information metric [@ Groisser & Murray
    dg/96];
    For complex functions,
(A, B):= ∫ dz A*(z) B(z) F(z) , F(z) > 0 for all z .
  * And other structure:
    It can always be used to define a norm by ||x||:= \(\langle\)x,
    x\(\rangle\)1/2, and thus a distance.
  * Inner product space: A pair (V,
    \(\langle\) ,  \(\rangle\)) as above; An example is any Hilbert space.
  @ General references:
    Dvurečenskij LMP(01) [criterion for completeness];
    Horváth JGP(10)
      [semi-definite and indefinite inner products, generalized Minkowski spaces].
  @ Partial inner product spaces: 
    Antoine et al AMP-a1203 [categorical aspects];
    Antoine & Trapani JPA(13)-a1210.
Related Concepts > see Bivector;
  Riesz Space [vector lattice].
  * Axial vector or pseudovector:
    A vector whose definition implies that the vector corresponding to a mirror reflected
    situation is the opposite of the mirror image of the original vector; > s.a.
    stochastic quantization [axial vector gauge theory];
    Wikipedia page.
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