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  2. Kabsch algorithm - Wikipedia

    en.wikipedia.org/wiki/Kabsch_algorithm

    Let P and Q be two sets, each containing N points in .We want to find the transformation from Q to P.For simplicity, we will consider the three-dimensional case (=).The sets P and Q can each be represented by N × 3 matrices with the first row containing the coordinates of the first point, the second row containing the coordinates of the second point, and so on, as shown in this matrix:

  3. Numerical methods for linear least squares - Wikipedia

    en.wikipedia.org/wiki/Numerical_methods_for...

    An alternative decomposition of X is the singular value decomposition (SVD) [1] = , where U is m by m orthogonal matrix, V is n by n orthogonal matrix and is an m by n matrix with all its elements outside of the main diagonal equal to 0.

  4. Pattern search (optimization) - Wikipedia

    en.wikipedia.org/wiki/Pattern_search_(optimization)

    Outside of such classes, pattern search is a heuristic that can provide useful approximate solutions for some issues, but can fail on others. Outside of such classes, pattern search is not an iterative method that converges to a solution; indeed, pattern-search methods can converge to non-stationary points on some relatively tame problems. [6] [7]

  5. Gaussian elimination - Wikipedia

    en.wikipedia.org/wiki/Gaussian_elimination

    Now through application of elementary row operations, find the reduced echelon form of this n × 2n matrix. The matrix A is invertible if and only if the left block can be reduced to the identity matrix I; in this case the right block of the final matrix is A −1. If the algorithm is unable to reduce the left block to I, then A is not invertible.

  6. Row echelon form - Wikipedia

    en.wikipedia.org/wiki/Row_echelon_form

    Each column containing a leading 1 has zeros in all entries above the leading 1. While a matrix may have several echelon forms, its reduced echelon form is unique. Given a matrix in reduced row echelon form, if one permutes the columns in order to have the leading 1 of the i th row in the i th column, one gets a matrix of the form

  7. Orthogonal Procrustes problem - Wikipedia

    en.wikipedia.org/wiki/Orthogonal_Procrustes_problem

    The orthogonal Procrustes problem [1] is a matrix approximation problem in linear algebra. In its classical form, one is given two matrices A {\displaystyle A} and B {\displaystyle B} and asked to find an orthogonal matrix Ω {\displaystyle \Omega } which most closely maps A {\displaystyle A} to B {\displaystyle B} .

  8. Stencil (numerical analysis) - Wikipedia

    en.wikipedia.org/wiki/Stencil_(numerical_analysis)

    The Crank–Nicolson stencil for a 1D problem. In mathematics, especially the areas of numerical analysis concentrating on the numerical solution of partial differential equations, a stencil is a geometric arrangement of a nodal group that relate to the point of interest by using a numerical approximation routine.

  9. Jacobi method - Wikipedia

    en.wikipedia.org/wiki/Jacobi_method

    Input: initial guess x (0) to the solution, (diagonal dominant) matrix A, right-hand side vector b, convergence criterion Output: solution when convergence is reached Comments: pseudocode based on the element-based formula above k = 0 while convergence not reached do for i := 1 step until n do σ = 0 for j := 1 step until n do if j ≠ i then ...