Search results
Results from the WOW.Com Content Network
The first two steps of the Gram–Schmidt process. In mathematics, particularly linear algebra and numerical analysis, the Gram–Schmidt process or Gram-Schmidt algorithm is a way of finding a set of two or more vectors that are perpendicular to each other.
Applying Gram–Schmidt one obtains an orthonormal basis (e i) for H. Let (H i) be the corresponding nested sequence of "coordinate" subspaces of H. The matrix a i,j expressing T with respect to (e i) is almost upper triangular, in the sense that the coefficients a i+1,i are the only nonzero sub-diagonal coefficients.
Cavendish experiment: Henry Cavendish: Measurement Gravitational constant: 1799 Voltaic pile: Alessandro Volta: Demonstration First electric battery: 1803 Young's interference experiment: Thomas Young: Confirmation Wave theory of light: 1819 Arago spot experiment François Arago: Confirmation Fresnel diffraction due to circular object 1838 ...
An alternative way to arrive at the same expressions is to take the first three derivatives of the curve r′(t), r′′(t), r′′′(t), and to apply the Gram-Schmidt process. The resulting ordered orthonormal basis is precisely the TNB frame. This procedure also generalizes to produce Frenet frames in higher dimensions.
The Gram matrix is symmetric in the case the inner product is real-valued; it is Hermitian in the general, complex case by definition of an inner product. The Gram matrix is positive semidefinite, and every positive semidefinite matrix is the Gramian matrix for some set of vectors. The fact that the Gramian matrix is positive-semidefinite can ...
In other words, the sequence is obtained from the sequence of monomials 1, x, x 2, … by the Gram–Schmidt process with respect to this inner product. Usually the sequence is required to be orthonormal , namely, P n , P n = 1 , {\displaystyle \langle P_{n},P_{n}\rangle =1,} however, other normalisations are sometimes used.
In mathematics, the Hamburger moment problem, named after Hans Ludwig Hamburger, is formulated as follows: given a sequence (m 0, m 1, m 2, ...), does there exist a positive Borel measure μ (for instance, the measure determined by the cumulative distribution function of a random variable) on the real line such that
For multiple source mixture signals, we can use kurtosis and Gram-Schmidt Orthogonalization (GSO) to recover the signals. Given M signal mixtures in an M-dimensional space, GSO project these data points onto an (M-1)-dimensional space by using the weight vector. We can guarantee the independence of the extracted signals with the use of GSO.