the m-by-n matrix to decompose (requires m >= n)
Use the Householder Bidiagonalization Algorithm to compute unitary matrices u and v such that u.
Use the Householder Bidiagonalization Algorithm to compute unitary matrices u and v such that u.t * a * v = b where matrix b is bidiagonal. The b matrix will only have non-zero elements on its main diagonal and its super diagonal (the diagonal above the main). This implementation computes b in-place in matrix a.
Matrix Computations: Algorithm 5.4.2 Householder Bidiagonalization
Use the Golub-Kahan Bidiagonalization Algorithm to compute unitary matrices u and v such that u.
Use the Golub-Kahan Bidiagonalization Algorithm to compute unitary matrices u and v such that u.t * a * v = b where matrix b is bidiagonal (nonzero elements on the main diagonal and the diagonal above it). Solve a * v = u * b by computing column vectors u_k and v_k and diagonals c and d. Use transposes of u and v since row access is more efficient than column access. Caveat: assumes bidiagonals elements are non-negative and need to add re-orthogonalization steps.
web.eecs.utk.edu/~dongarra/etemplates/node198.html
Show the flaw by printing the error message.
Show the flaw by printing the error message.
the method where the error occurred
the error message
This class is used to compute the Singlar Value Decomposition (SVD) of matrix 'a', i.e., decompose matrix 'a' into 'u * b * v.t'.