sla.svd              package:RScaLAPACK              R Documentation

_S_i_n_g_u_l_a_r _V_a_l_u_e _D_e_c_o_m_p_o_s_i_t_i_o_n _o_f _a _M_a_t_r_i_x

_D_e_s_c_r_i_p_t_i_o_n:

     Compute the singular-value decomposition of a rectangular matrix.

_U_s_a_g_e:

     sla.svd(A, nu, nv, NPROWS, NPCOLS, MB, RFLAG, SPAWN)

_A_r_g_u_m_e_n_t_s:

       A: A matrix whose SVD decomposition is to be computed 

      nu: The number of left singular vectors to be computed.  Must be
          either '0' or 'min(nrow(x), ncol(x)'.

      nv: The number of right singular vectors to be computed.  Must be
          either '0' or 'min(nrow(x), ncol(x)'.

  NPROWS: Number of Process Rows in the Process Grid. 

  NPCOLS: Number of Process Cols in the Process Grid. 

      MB: Block Size. 

   RFLAG: Flag saying whether the Process Grid should be released after
          computation.

   SPAWN: Flag saying whether a new Process Grid should be spawned.

_D_e_t_a_i_l_s:

     If the number of processor rows and columns are both zero, one
     processor is used.  If the number of processor rows is nonzero and
     the number of processor columns is zero, then the processor rows
     is taken to be a  number of processors, and a grid is made
     accordingly.

_R_e_f_e_r_e_n_c_e_s:

     <URL: http://www.netlib.org/scalapack/>

     <URL:
     http://mathworld.wolfram.com/SingularValueDecomposition.html>

_S_e_e _A_l_s_o:

     'sla.solve' the SCALAPACK version of the R function 'solve'

     'sla.qr' the SCALAPACK version of the R function 'qr'

     'La.svd' the LAPACK implementation of singular value
     decomposition.

_E_x_a_m_p_l_e_s:

     library(RScaLAPACK)
     rnorm(100)->x
     dim(x)=c(5,20)
     sla.svd(x)

