All Known Implementing Classes:
SVD

public interface SVD<T>
Singular Value Decomposition.

For an m-by-n matrix A, the singular value decomposition is an m-by-(m or n) orthogonal matrix U, a (m or n)-by-n diagonal matrix S, and an n-by-n orthogonal matrix V so that A = U*S*V'.

The singular values, sigma[k] = S[k][k], are ordered so that sigma[0] >= sigma[1] >= ... >= sigma[n-1].

The singular value decompostion always exists, so the constructor will never fail. The matrix condition number and the effective numerical rank can be computed from this decomposition.

  • Field Details

    • THRESHOLD

      static final int THRESHOLD
      See Also:
    • MATRIX

      static final SVD<Matrix> MATRIX
    • INSTANCE

      static final SVD<Matrix> INSTANCE
    • UJMP

      static final SVD<Matrix> UJMP
    • MATRIXSMALLSINGLETHREADED

      static final SVD<Matrix> MATRIXSMALLSINGLETHREADED
    • MATRIXLARGESINGLETHREADED

      static final SVD<Matrix> MATRIXLARGESINGLETHREADED
    • MATRIXSMALLMULTITHREADED

      static final SVD<Matrix> MATRIXSMALLMULTITHREADED
    • MATRIXLARGEMULTITHREADED

      static final SVD<Matrix> MATRIXLARGEMULTITHREADED
  • Method Details

    • calc

      T[] calc(T source)