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When successive powers of a matrix T become small (that is, when all of the entries of T approach zero, upon raising T to successive powers), the matrix T converges to the zero matrix. A regular splitting of a non-singular matrix A results in a convergent matrix T. A semi-convergent splitting of a matrix A results in a semi-convergent matrix T.
In chemical analysis, matrix refers to the components of a sample other than the analyte [1] of interest. The matrix can have a considerable effect on the way the analysis is conducted and the quality of the results are obtained; such effects are called matrix effects. [ 2 ]
An "almost" triangular matrix, for example, an upper Hessenberg matrix has zero entries below the first subdiagonal. Hollow matrix: A square matrix whose main diagonal comprises only zero elements. Integer matrix: A matrix whose entries are all integers. Logical matrix: A matrix with all entries either 0 or 1.
In chemistry a convergent synthesis is a strategy that aims to improve the efficiency of multistep synthesis, most often in organic synthesis. In this type of synthesis several individual pieces of a complex molecule are synthesized in stage one, and then in stage two these pieces are combined to form the final product. [ 1 ]
An iterative method with a given iteration matrix is called convergent if the following holds lim k → ∞ C k = 0. {\displaystyle \lim _{k\rightarrow \infty }C^{k}=0.} An important theorem states that for a given iterative method and its iteration matrix C {\displaystyle C} it is convergent if and only if its spectral radius ρ ( C ...
The theorem states that if you have an infinite matrix of non-negative real numbers , such that the rows are weakly increasing and each is bounded , where the bounds are summable < then, for each column, the non decreasing column sums , are bounded hence convergent, and the limit of the column sums is equal to the sum of the "limit column ...
Conjugate gradient, assuming exact arithmetic, converges in at most n steps, where n is the size of the matrix of the system (here n = 2). In mathematics, the conjugate gradient method is an algorithm for the numerical solution of particular systems of linear equations, namely those whose matrix is positive-semidefinite.
For example, we learn that if ƒ is continuous at t, then the Fourier series of ƒ cannot converge to a value different from ƒ(t). It may either converge to ƒ ( t ) or diverge. This is because, if S N ( f ; t ) {\displaystyle S_{N}(f;t)} converges to some value x , it is also summable to it, so from the first summability property above, x ...