dposvxx (l)  Linux Man Pages
dposvxx: DPOSVXX use the Cholesky factorization A = U**T*U or A = L*L**T to compute the solution to a double precision system of linear equations A * X = B, where A is an NbyN symmetric positive definite matrix and X and B are NbyNRHS matrices
NAME
DPOSVXX  DPOSVXX use the Cholesky factorization A = U**T*U or A = L*L**T to compute the solution to a double precision system of linear equations A * X = B, where A is an NbyN symmetric positive definite matrix and X and B are NbyNRHS matricesSYNOPSIS
 SUBROUTINE DPOSVXX(
 FACT, UPLO, N, NRHS, A, LDA, AF, LDAF, EQUED, S, B, LDB, X, LDX, RCOND, RPVGRW, BERR, N_ERR_BNDS, ERR_BNDS_NORM, ERR_BNDS_COMP, NPARAMS, PARAMS, WORK, IWORK, INFO )
 IMPLICIT NONE
 CHARACTER EQUED, FACT, UPLO
 INTEGER INFO, LDA, LDAF, LDB, LDX, N, NRHS, NPARAMS, N_ERR_BNDS
 DOUBLE PRECISION RCOND, RPVGRW
 INTEGER IWORK( * )
 DOUBLE PRECISION A( LDA, * ), AF( LDAF, * ), B( LDB, * ), X( LDX, * ), WORK( * )
 DOUBLE PRECISION S( * ), PARAMS( * ), BERR( * ), ERR_BNDS_NORM( NRHS, * ), ERR_BNDS_COMP( NRHS, * )
PURPOSE
DPOSVXX uses the Cholesky factorization A
to compute the solution to a double precision system of linear equations
A
and X and B are NbyNRHS matrices.
If requested, both normwise and maximum componentwise error bounds
are returned. DPOSVXX will return a solution with a tiny
guaranteed error
precision)
case a warning is returned. Relevant condition numbers also are
calculated and returned.
DPOSVXX accepts userprovided factorizations and equilibration
factors;
Solving with refinement and using a factorization from a previous
DPOSVXX call will also produce a solution with either O(eps)
errors or warnings, but we cannot make that claim for general
userprovided factorizations and equilibration factors if they
differ from what DPOSVXX would itself produce.
DESCRIPTION
The following steps are performed:
1. If FACT
the system:
Whether or not the system will be equilibrated depends on the
scaling of the matrix A, but if equilibration is used, A is
overwritten by diag(S)*A*diag(S)
2. If FACT
factor the matrix A
A
A
where U is an upper triangular matrix and L is a lower triangular
matrix.
3. If the leading ibyi principal minor is not positive definite,
then the routine returns with INFO
form of A is used to estimate the condition number of the matrix
A
is less than machine precision, the routine still goes on to solve
for X and compute error bounds as described below.
4. The system of equations is solved for X using the factored form
of A.
5. By default
the routine will use iterative refinement to try to get a small
error and error bounds.
least twice the working precision.
6. If equilibration was used, the matrix X is premultiplied by
diag(S)
equilibration.
ARGUMENTS
Some optional parameters are bundled in the PARAMS array. These settings determine how refinement is performed, but often the defaults are acceptable. If the defaults are acceptable, users can pass NPARAMS = 0 which prevents the source code from accessing the PARAMS argument. FACT (input) CHARACTER*1

Specifies whether or not the factored form of the matrix A is
supplied on entry, and if not, whether the matrix A should be
equilibrated before it is factored.
= aqFaq: On entry, AF contains the factored form of A.
If EQUED is not aqNaq, the matrix A has been
equilibrated with scaling factors given by S.
A and AF are not modified.
= aqNaq: The matrix A will be copied to AF and factored.
= aqEaq: The matrix A will be equilibrated if necessary, then copied to AF and factored.  UPLO (input) CHARACTER*1

= aqUaq: Upper triangle of A is stored;
= aqLaq: Lower triangle of A is stored.  N (input) INTEGER
 The number of linear equations, i.e., the order of the matrix A. N >= 0.
 NRHS (input) INTEGER
 The number of right hand sides, i.e., the number of columns of the matrices B and X. NRHS >= 0.
 A (input/output) DOUBLE PRECISION array, dimension (LDA,N)
 On entry, the symmetric matrix A, except if FACT = aqFaq and EQUED = aqYaq, then A must contain the equilibrated matrix diag(S)*A*diag(S). If UPLO = aqUaq, the leading NbyN upper triangular part of A contains the upper triangular part of the matrix A, and the strictly lower triangular part of A is not referenced. If UPLO = aqLaq, the leading NbyN lower triangular part of A contains the lower triangular part of the matrix A, and the strictly upper triangular part of A is not referenced. A is not modified if FACT = aqFaq or aqNaq, or if FACT = aqEaq and EQUED = aqNaq on exit. On exit, if FACT = aqEaq and EQUED = aqYaq, A is overwritten by diag(S)*A*diag(S).
 LDA (input) INTEGER
 The leading dimension of the array A. LDA >= max(1,N).
 AF (input or output) DOUBLE PRECISION array, dimension (LDAF,N)
 If FACT = aqFaq, then AF is an input argument and on entry contains the triangular factor U or L from the Cholesky factorization A = U**T*U or A = L*L**T, in the same storage format as A. If EQUED .ne. aqNaq, then AF is the factored form of the equilibrated matrix diag(S)*A*diag(S). If FACT = aqNaq, then AF is an output argument and on exit returns the triangular factor U or L from the Cholesky factorization A = U**T*U or A = L*L**T of the original matrix A. If FACT = aqEaq, then AF is an output argument and on exit returns the triangular factor U or L from the Cholesky factorization A = U**T*U or A = L*L**T of the equilibrated matrix A (see the description of A for the form of the equilibrated matrix).
 LDAF (input) INTEGER
 The leading dimension of the array AF. LDAF >= max(1,N).
 EQUED (input or output) CHARACTER*1

Specifies the form of equilibration that was done.
= aqNaq: No equilibration (always true if FACT = aqNaq).
= aqYaq: Both row and column equilibration, i.e., A has been replaced by diag(S) * A * diag(S). EQUED is an input argument if FACT = aqFaq; otherwise, it is an output argument.  S (input or output) DOUBLE PRECISION array, dimension (N)
 The row scale factors for A. If EQUED = aqYaq, A is multiplied on the left and right by diag(S). S is an input argument if FACT = aqFaq; otherwise, S is an output argument. If FACT = aqFaq and EQUED = aqYaq, each element of S must be positive. If S is output, each element of S is a power of the radix. If S is input, each element of S should be a power of the radix to ensure a reliable solution and error estimates. Scaling by powers of the radix does not cause rounding errors unless the result underflows or overflows. Rounding errors during scaling lead to refining with a matrix that is not equivalent to the input matrix, producing error estimates that may not be reliable.
 B (input/output) DOUBLE PRECISION array, dimension (LDB,NRHS)
 On entry, the NbyNRHS right hand side matrix B. On exit, if EQUED = aqNaq, B is not modified; if EQUED = aqYaq, B is overwritten by diag(S)*B;
 LDB (input) INTEGER
 The leading dimension of the array B. LDB >= max(1,N).
 X (output) DOUBLE PRECISION array, dimension (LDX,NRHS)
 If INFO = 0, the NbyNRHS solution matrix X to the original system of equations. Note that A and B are modified on exit if EQUED .ne. aqNaq, and the solution to the equilibrated system is inv(diag(S))*X.
 LDX (input) INTEGER
 The leading dimension of the array X. LDX >= max(1,N).
 RCOND (output) DOUBLE PRECISION
 Reciprocal scaled condition number. This is an estimate of the reciprocal Skeel condition number of the matrix A after equilibration (if done). If this is less than the machine precision (in particular, if it is zero), the matrix is singular to working precision. Note that the error may still be small even if this number is very small and the matrix appears ill conditioned.
 RPVGRW (output) DOUBLE PRECISION
 Reciprocal pivot growth. On exit, this contains the reciprocal pivot growth factor norm(A)/norm(U). The "max absolute element" norm is used. If this is much less than 1, then the stability of the LU factorization of the (equilibrated) matrix A could be poor. This also means that the solution X, estimated condition numbers, and error bounds could be unreliable. If factorization fails with 0<INFO<=N, then this contains the reciprocal pivot growth factor for the leading INFO columns of A.
 BERR (output) DOUBLE PRECISION array, dimension (NRHS)
 Componentwise relative backward error. This is the componentwise relative backward error of each solution vector X(j) (i.e., the smallest relative change in any element of A or B that makes X(j) an exact solution). N_ERR_BNDS (input) INTEGER Number of error bounds to return for each right hand side and each type (normwise or componentwise). See ERR_BNDS_NORM and ERR_BNDS_COMP below.
 ERR_BNDS_NORM (output) DOUBLE PRECISION array, dimension (NRHS, N_ERR_BNDS)
 For each righthand side, this array contains information about various error bounds and condition numbers corresponding to the normwise relative error, which is defined as follows: Normwise relative error in the ith solution vector: max_j (abs(XTRUE(j,i)  X(j,i)))  max_j abs(X(j,i)) The array is indexed by the type of error information as described below. There currently are up to three pieces of information returned. The first index in ERR_BNDS_NORM(i,:) corresponds to the ith righthand side. The second index in ERR_BNDS_NORM(:,err) contains the following three fields: err = 1 "Trust/donaqt trust" boolean. Trust the answer if the reciprocal condition number is less than the threshold sqrt(n) * dlamch(aqEpsilonaq). err = 2 "Guaranteed" error bound: The estimated forward error, almost certainly within a factor of 10 of the true error so long as the next entry is greater than the threshold sqrt(n) * dlamch(aqEpsilonaq). This error bound should only be trusted if the previous boolean is true. err = 3 Reciprocal condition number: Estimated normwise reciprocal condition number. Compared with the threshold sqrt(n) * dlamch(aqEpsilonaq) to determine if the error estimate is "guaranteed". These reciprocal condition numbers are 1 / (norm(Z^{1},inf) * norm(Z,inf)) for some appropriately scaled matrix Z. Let Z = S*A, where S scales each row by a power of the radix so all absolute row sums of Z are approximately 1. See Lapack Working Note 165 for further details and extra cautions.
 ERR_BNDS_COMP (output) DOUBLE PRECISION array, dimension (NRHS, N_ERR_BNDS)
 For each righthand side, this array contains information about various error bounds and condition numbers corresponding to the componentwise relative error, which is defined as follows: Componentwise relative error in the ith solution vector: abs(XTRUE(j,i)  X(j,i)) max_j  abs(X(j,i)) The array is indexed by the righthand side i (on which the componentwise relative error depends), and the type of error information as described below. There currently are up to three pieces of information returned for each righthand side. If componentwise accuracy is not requested (PARAMS(3) = 0.0), then ERR_BNDS_COMP is not accessed. If N_ERR_BNDS .LT. 3, then at most the first (:,N_ERR_BNDS) entries are returned. The first index in ERR_BNDS_COMP(i,:) corresponds to the ith righthand side. The second index in ERR_BNDS_COMP(:,err) contains the following three fields: err = 1 "Trust/donaqt trust" boolean. Trust the answer if the reciprocal condition number is less than the threshold sqrt(n) * dlamch(aqEpsilonaq). err = 2 "Guaranteed" error bound: The estimated forward error, almost certainly within a factor of 10 of the true error so long as the next entry is greater than the threshold sqrt(n) * dlamch(aqEpsilonaq). This error bound should only be trusted if the previous boolean is true. err = 3 Reciprocal condition number: Estimated componentwise reciprocal condition number. Compared with the threshold sqrt(n) * dlamch(aqEpsilonaq) to determine if the error estimate is "guaranteed". These reciprocal condition numbers are 1 / (norm(Z^{1},inf) * norm(Z,inf)) for some appropriately scaled matrix Z. Let Z = S*(A*diag(x)), where x is the solution for the current righthand side and S scales each row of A*diag(x) by a power of the radix so all absolute row sums of Z are approximately 1. See Lapack Working Note 165 for further details and extra cautions. NPARAMS (input) INTEGER Specifies the number of parameters set in PARAMS. If .LE. 0, the PARAMS array is never referenced and default values are used.
 PARAMS (input / output) DOUBLE PRECISION array, dimension NPARAMS

Specifies algorithm parameters. If an entry is .LT. 0.0, then
that entry will be filled with default value used for that
parameter. Only positions up to NPARAMS are accessed; defaults
are used for highernumbered parameters.
PARAMS(LA_LINRX_ITREF_I = 1) : Whether to perform iterative
refinement or not.
Default: 1.0D+0
= 0.0 : No refinement is performed, and no error bounds are computed. = 1.0 : Use the extraprecise refinement algorithm. (other values are reserved for future use) PARAMS(LA_LINRX_ITHRESH_I = 2) : Maximum number of residual computations allowed for refinement. Default: 10
Aggressive: Set to 100 to permit convergence using approximate factorizations or factorizations other than LU. If the factorization uses a technique other than Gaussian elimination, the guarantees in err_bnds_norm and err_bnds_comp may no longer be trustworthy. PARAMS(LA_LINRX_CWISE_I = 3) : Flag determining if the code will attempt to find a solution with small componentwise relative error in the doubleprecision algorithm. Positive is true, 0.0 is false. Default: 1.0 (attempt componentwise convergence)  WORK (workspace) DOUBLE PRECISION array, dimension (4*N)
 IWORK (workspace) INTEGER array, dimension (N)
 INFO (output) INTEGER

= 0: Successful exit. The solution to every righthand side is guaranteed. < 0: If INFO = i, the ith argument had an illegal value
> 0 and <= N: U(INFO,INFO) is exactly zero. The factorization has been completed, but the factor U is exactly singular, so the solution and error bounds could not be computed. RCOND = 0 is returned. = N+J: The solution corresponding to the Jth righthand side is not guaranteed. The solutions corresponding to other right hand sides K with K > J may not be guaranteed as well, but only the first such righthand side is reported. If a small componentwise error is not requested (PARAMS(3) = 0.0) then the Jth righthand side is the first with a normwise error bound that is not guaranteed (the smallest J such that ERR_BNDS_NORM(J,1) = 0.0). By default (PARAMS(3) = 1.0) the Jth righthand side is the first with either a normwise or componentwise error bound that is not guaranteed (the smallest J such that either ERR_BNDS_NORM(J,1) = 0.0 or ERR_BNDS_COMP(J,1) = 0.0). See the definition of ERR_BNDS_NORM(:,1) and ERR_BNDS_COMP(:,1). To get information about all of the righthand sides check ERR_BNDS_NORM or ERR_BNDS_COMP.