sla.chol2inv           package:RScaLAPACK           R Documentation

_I_n_v_e_r_t _a _M_a_t_r_i_x _f_r_o_m _t_h_e _C_h_o_l_e_s_k_y _D_e_c_o_m_p_o_s_i_t_i_o_n (_U_s_i_n_g _S_c_a_L_A_P_A_C_K)

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

     Invert a symmetric, positive definite, square matrix from its
     Choleski decomposition using ScaLAPACK.

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

     sla.chol2inv(A, NPROWS, NPCOLS, MB, RFLAG, SPAWN)

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

       A: a matrix.  The upper triangle should hold the Choleski
          Decomposition to compute the inverse of.

  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.

_V_a_l_u_e:

     The inverse of the decomposed matrix is returned.

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

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

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

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

     'chol2inv' for the normal R version.

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

     library(RScaLAPACK)
     rnorm(100)->x
     dim(x)=c(10,10)
     # First, generate a symmetric, positive definite matrix
     sla.chol2inv(x)->y
     print(y)
     # Invert
     sla.solve(y)->y2
     # Perform a Choleski decomposition
     sla.chol(y2)->x

