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Condition numbers can also be defined for nonlinear functions, and can be computed using calculus.The condition number varies with the point; in some cases one can use the maximum (or supremum) condition number over the domain of the function or domain of the question as an overall condition number, while in other cases the condition number at a particular point is of more interest.
that is, as a Gramian matrix for powers of x. It arises in the least squares approximation of arbitrary functions by polynomials. The Hilbert matrices are canonical examples of ill-conditioned matrices, being notoriously difficult to use in numerical computation. For example, the 2-norm condition number of the matrix above is about 4.8 × 10 5.
In mathematics, and in particular linear algebra, the Moore–Penrose inverse of a matrix , often called the pseudoinverse, is the most widely known generalization of the inverse matrix. [ 1 ] It was independently described by E. H. Moore in 1920, [ 2 ] Arne Bjerhammar in 1951, [ 3 ] and Roger Penrose in 1955. [ 4 ]
Preconditioner. In mathematics, preconditioning is the application of a transformation, called the preconditioner, that conditions a given problem into a form that is more suitable for numerical solving methods. Preconditioning is typically related to reducing a condition number of the problem.
The stiffness matrix is the n -element square matrix A defined by. By defining the vector F with components the coefficients ui are determined by the linear system Au = F. The stiffness matrix is symmetric, i.e. Aij = Aji, so all its eigenvalues are real. Moreover, it is a strictly positive-definite matrix, so that the system Au = F always has ...
The condition number is computed by finding the maximum singular value divided by the minimum singular value of the design matrix. [9] In the context of collinear variables, the variance inflation factor is the condition number for a particular coefficient.
Symplectic matrix. In mathematics, a symplectic matrix is a matrix with real entries that satisfies the condition. (1) where denotes the transpose of and is a fixed nonsingular, skew-symmetric matrix. This definition can be extended to matrices with entries in other fields, such as the complex numbers, finite fields, p -adic numbers, and ...
Skew-symmetric matrix. For matrices with antisymmetry over the complex number field, see Skew-Hermitian matrix. In mathematics, particularly in linear algebra, a skew-symmetric (or antisymmetric or antimetric[ 1 ]) matrix is a square matrix whose transpose equals its negative. That is, it satisfies the condition [ 2 ]: p. 38.