The calculation of the Singular Value Decomposition (SVD) of a matrix is at the basis of many computations and approaches in applied science. One example is the regularized solution of linear systems of equations. Another is Principal Component Analysis.
Many times, the applications requiring the SVD calculation deal with large matrices and/or request the SVD computation in an iterative process.
Fortunately, the SVD can be quickly computed in CUDA using the routines provided in the cuSOLVE...

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# SVD

# Singular values calculation only of a real matrix with CUDA

Besides the full SVD of a matrix, see SVD of a real matrix, by cusolverDnSgesvd, it is possible also to calculate only the singular values of a matrix.
On our GitHub website, we report a sample code with two calls to cusolverDnSgesvd, one performing the singular values calculation only
cusolverDnSgesvd(solver_handle, 'N', 'N', M, N, d_A, M, d_S, NULL, M, NULL, N, work, work_size, NULL, devInfo)
and one performing the full SVD calculation
cusolverDnSgesvd(solver_handle, 'A', 'A', M, N, d_...

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