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In mathematics, positive semidefinite may refer to: Positive semidefinite function; Positive semidefinite matrix; Positive semidefinite quadratic form;
U and P commute, where we have the polar decomposition A = UP with a unitary matrix U and some positive semidefinite matrix P. A commutes with some normal matrix N with distinct [clarification needed] eigenvalues. σ i = | λ i | for all 1 ≤ i ≤ n where A has singular values σ 1 ≥ ⋯ ≥ σ n and has eigenvalues that are indexed with ...
The conjugate gradient method can be applied to an arbitrary n-by-m matrix by applying it to normal equations A T A and right-hand side vector A T b, since A T A is a symmetric positive-semidefinite matrix for any A. The result is conjugate gradient on the normal equations (CGN or CGNR). A T Ax = A T b
In mathematics, a symmetric matrix with real entries is positive-definite if the real number is positive for every nonzero real column vector , where is the row vector transpose of . [1] More generally, a Hermitian matrix (that is, a complex matrix equal to its conjugate transpose) is positive-definite if the real number is positive for every nonzero complex column vector , where denotes the ...
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 be seen from the following simple derivation:
This implies that at a local minimum the Hessian is positive-semidefinite, and at a local maximum the Hessian is negative-semidefinite. For positive-semidefinite and negative-semidefinite Hessians the test is inconclusive (a critical point where the Hessian is semidefinite but not definite may be a local extremum or a saddle point).
Set P = ∅. Set R = {1, ..., n}. Set x to an all-zero vector of dimension n. Set w = A T (y − Ax). Let w R denote the sub-vector with indexes from R; Main loop: while R ≠ ∅ and max(w R) > ε: Let j in R be the index of max(w R) in w. Add j to P. Remove j from R. Let A P be A restricted to the variables included in P. Let s be vector of ...
The FIM is a N × N positive semidefinite matrix. If it is positive definite, then it defines a Riemannian metric [11] on the N-dimensional parameter space. The topic information geometry uses this to connect Fisher information to differential geometry, and in that context, this metric is known as the Fisher information metric.