Search results
Results from the WOW.Com Content Network
The first idea behind the Proper Orthogonal Decomposition (POD), as it was originally formulated in the domain of fluid dynamics to analyze turbulences, is to decompose a random vector field u(x, t) into a set of deterministic spatial functions Φ k (x) modulated by random time coefficients a k (t) so that:
The Helmholtz decomposition can be generalized by reducing the regularity assumptions (the need for the existence of strong derivatives). Suppose Ω is a bounded, simply-connected, Lipschitz domain. Every square-integrable vector field u ∈ (L 2 (Ω)) 3 has an orthogonal decomposition: [19] [20] [21]
In linear algebra, the complete orthogonal decomposition is a matrix decomposition. [ 1 ] [ 2 ] It is similar to the singular value decomposition , but typically somewhat [ 3 ] cheaper to compute and in particular much cheaper and easier to update when the original matrix is slightly altered.
Decomposition: This is a version of Schur decomposition where and only contain real numbers. One can always write A = V S V T {\displaystyle A=VSV^{\mathsf {T}}} where V is a real orthogonal matrix , V T {\displaystyle V^{\mathsf {T}}} is the transpose of V , and S is a block upper triangular matrix called the real Schur form .
In the theory of stochastic processes, the Karhunen–Loève theorem (named after Kari Karhunen and Michel Loève), also known as the Kosambi–Karhunen–Loève theorem [1] [2] states that a stochastic process can be represented as an infinite linear combination of orthogonal functions, analogous to a Fourier series representation of a ...
The solution is the product . [3] This intuitively makes sense because an orthogonal matrix would have the decomposition where is the identity matrix, so that if = then the product = amounts to replacing the singular values with ones.
The origin and all events on the light cone are self-orthogonal. When a time event and a space event evaluate to zero under the bilinear form, then they are hyperbolic-orthogonal. This terminology stems from the use of conjugate hyperbolas in the pseudo-Euclidean plane: conjugate diameters of these hyperbolas are hyperbolic-orthogonal.
In mathematics, the polar decomposition of a square real or complex matrix is a factorization of the form =, where is a unitary matrix and is a positive semi-definite Hermitian matrix (is an orthogonal matrix and is a positive semi-definite symmetric matrix in the real case), both square and of the same size.