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  2. Riesz's lemma - Wikipedia

    en.wikipedia.org/wiki/Riesz's_lemma

    However, every finite dimensional normed space is a reflexive Banach space, so Riesz’s lemma does holds for = when the normed space is finite-dimensional, as will now be shown. When the dimension of X {\displaystyle X} is finite then the closed unit ball B ⊆ X {\displaystyle B\subseteq X} is compact.

  3. Orthonormality - Wikipedia

    en.wikipedia.org/wiki/Orthonormality

    The Gram-Schmidt theorem, together with the axiom of choice, guarantees that every vector space admits an orthonormal basis. This is possibly the most significant use of orthonormality, as this fact permits operators on inner-product spaces to be discussed in terms of their action on the space's orthonormal basis vectors. What results is a deep ...

  4. Spherical harmonics - Wikipedia

    en.wikipedia.org/wiki/Spherical_harmonics

    Conversely, the spaces H ℓ are precisely the eigenspaces of Δ S n−1. In particular, an application of the spectral theorem to the Riesz potential gives another proof that the spaces H ℓ are pairwise orthogonal and complete in L 2 (S n−1).

  5. Orthogonality (mathematics) - Wikipedia

    en.wikipedia.org/wiki/Orthogonality_(mathematics)

    In Euclidean space, two vectors are orthogonal if and only if their dot product is zero, i.e. they make an angle of 90° (radians), or one of the vectors is zero. [4] Hence orthogonality of vectors is an extension of the concept of perpendicular vectors to spaces of any dimension.

  6. Quotient of subspace theorem - Wikipedia

    en.wikipedia.org/wiki/Quotient_of_subspace_theorem

    In mathematics, the quotient of subspace theorem is an important property of finite-dimensional normed spaces, discovered by Vitali Milman. [1] Let (X, ||·||) be an N-dimensional normed space. There exist subspaces Z ⊂ Y ⊂ X such that the following holds:

  7. Orthogonalization - Wikipedia

    en.wikipedia.org/wiki/Orthogonalization

    In linear algebra, orthogonalization is the process of finding a set of orthogonal vectors that span a particular subspace.Formally, starting with a linearly independent set of vectors {v 1, ... , v k} in an inner product space (most commonly the Euclidean space R n), orthogonalization results in a set of orthogonal vectors {u 1, ... , u k} that generate the same subspace as the vectors v 1 ...

  8. Norm (mathematics) - Wikipedia

    en.wikipedia.org/wiki/Norm_(mathematics)

    Other examples of infinite-dimensional normed vector spaces can be found in the Banach space article. Generally, these norms do not give the same topologies. For example, an infinite-dimensional ℓ p {\displaystyle \ell ^{p}} space gives a strictly finer topology than an infinite-dimensional ℓ q {\displaystyle \ell ^{q}} space when p < q ...

  9. L-semi-inner product - Wikipedia

    en.wikipedia.org/wiki/L-semi-inner_product

    Conversely, if is a normed vector space with the norm ‖ ‖ then there always exists a (not necessarily unique) semi-inner-product on that is consistent with the norm on in the sense that [1] ‖ ‖ = [,] /, .

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