SUBROUTINE SPTSVX( FACT, N, NRHS, D, E, DF, EF, B, LDB, X, LDX, $ RCOND, FERR, BERR, WORK, INFO ) * * -- LAPACK routine (version 3.1) -- * Univ. of Tennessee, Univ. of California Berkeley and NAG Ltd.. * November 2006 * * .. Scalar Arguments .. CHARACTER FACT INTEGER INFO, LDB, LDX, N, NRHS REAL RCOND * .. * .. Array Arguments .. REAL B( LDB, * ), BERR( * ), D( * ), DF( * ), $ E( * ), EF( * ), FERR( * ), WORK( * ), $ X( LDX, * ) * .. * * Purpose * ======= * * SPTSVX uses the factorization A = L*D*L**T to compute the solution * to a real system of linear equations A*X = B, where A is an N-by-N * symmetric positive definite tridiagonal matrix and X and B are * N-by-NRHS matrices. * * Error bounds on the solution and a condition estimate are also * provided. * * Description * =========== * * The following steps are performed: * * 1. If FACT = 'N', the matrix A is factored as A = L*D*L**T, where L * is a unit lower bidiagonal matrix and D is diagonal. The * factorization can also be regarded as having the form * A = U**T*D*U. * * 2. If the leading i-by-i principal minor is not positive definite, * then the routine returns with INFO = i. Otherwise, the factored * form of A is used to estimate the condition number of the matrix * A. If the reciprocal of the condition number is less than machine * precision, INFO = N+1 is returned as a warning, but the routine * still goes on to solve for X and compute error bounds as * described below. * * 3. The system of equations is solved for X using the factored form * of A. * * 4. Iterative refinement is applied to improve the computed solution * matrix and calculate error bounds and backward error estimates * for it. * * Arguments * ========= * * FACT (input) CHARACTER*1 * Specifies whether or not the factored form of A has been * supplied on entry. * = 'F': On entry, DF and EF contain the factored form of A. * D, E, DF, and EF will not be modified. * = 'N': The matrix A will be copied to DF and EF and * factored. * * N (input) INTEGER * 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. * * D (input) REAL array, dimension (N) * The n diagonal elements of the tridiagonal matrix A. * * E (input) REAL array, dimension (N-1) * The (n-1) subdiagonal elements of the tridiagonal matrix A. * * DF (input or output) REAL array, dimension (N) * If FACT = 'F', then DF is an input argument and on entry * contains the n diagonal elements of the diagonal matrix D * from the L*D*L**T factorization of A. * If FACT = 'N', then DF is an output argument and on exit * contains the n diagonal elements of the diagonal matrix D * from the L*D*L**T factorization of A. * * EF (input or output) REAL array, dimension (N-1) * If FACT = 'F', then EF is an input argument and on entry * contains the (n-1) subdiagonal elements of the unit * bidiagonal factor L from the L*D*L**T factorization of A. * If FACT = 'N', then EF is an output argument and on exit * contains the (n-1) subdiagonal elements of the unit * bidiagonal factor L from the L*D*L**T factorization of A. * * B (input) REAL array, dimension (LDB,NRHS) * The N-by-NRHS right hand side matrix B. * * LDB (input) INTEGER * The leading dimension of the array B. LDB >= max(1,N). * * X (output) REAL array, dimension (LDX,NRHS) * If INFO = 0 of INFO = N+1, the N-by-NRHS solution matrix X. * * LDX (input) INTEGER * The leading dimension of the array X. LDX >= max(1,N). * * RCOND (output) REAL * The reciprocal condition number of the matrix A. If RCOND * is less than the machine precision (in particular, if * RCOND = 0), the matrix is singular to working precision. * This condition is indicated by a return code of INFO > 0. * * FERR (output) REAL array, dimension (NRHS) * The forward error bound for each solution vector * X(j) (the j-th column of the solution matrix X). * If XTRUE is the true solution corresponding to X(j), FERR(j) * is an estimated upper bound for the magnitude of the largest * element in (X(j) - XTRUE) divided by the magnitude of the * largest element in X(j). * * BERR (output) REAL array, dimension (NRHS) * 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). * * WORK (workspace) REAL array, dimension (2*N) * * INFO (output) INTEGER * = 0: successful exit * < 0: if INFO = -i, the i-th argument had an illegal value * > 0: if INFO = i, and i is * <= N: the leading minor of order i of A is * not positive definite, so the factorization * could not be completed, and the solution has not * been computed. RCOND = 0 is returned. * = N+1: U is nonsingular, but RCOND is less than machine * precision, meaning that the matrix is singular * to working precision. Nevertheless, the * solution and error bounds are computed because * there are a number of situations where the * computed solution can be more accurate than the * value of RCOND would suggest. * * ===================================================================== * * .. Parameters .. REAL ZERO PARAMETER ( ZERO = 0.0E+0 ) * .. * .. Local Scalars .. LOGICAL NOFACT REAL ANORM * .. * .. External Functions .. LOGICAL LSAME REAL SLAMCH, SLANST EXTERNAL LSAME, SLAMCH, SLANST * .. * .. External Subroutines .. EXTERNAL SCOPY, SLACPY, SPTCON, SPTRFS, SPTTRF, SPTTRS, $ XERBLA * .. * .. Intrinsic Functions .. INTRINSIC MAX * .. * .. Executable Statements .. * * Test the input parameters. * INFO = 0 NOFACT = LSAME( FACT, 'N' ) IF( .NOT.NOFACT .AND. .NOT.LSAME( FACT, 'F' ) ) THEN INFO = -1 ELSE IF( N.LT.0 ) THEN INFO = -2 ELSE IF( NRHS.LT.0 ) THEN INFO = -3 ELSE IF( LDB.LT.MAX( 1, N ) ) THEN INFO = -9 ELSE IF( LDX.LT.MAX( 1, N ) ) THEN INFO = -11 END IF IF( INFO.NE.0 ) THEN CALL XERBLA( 'SPTSVX', -INFO ) RETURN END IF * IF( NOFACT ) THEN * * Compute the L*D*L' (or U'*D*U) factorization of A. * CALL SCOPY( N, D, 1, DF, 1 ) IF( N.GT.1 ) $ CALL SCOPY( N-1, E, 1, EF, 1 ) CALL SPTTRF( N, DF, EF, INFO ) * * Return if INFO is non-zero. * IF( INFO.GT.0 )THEN RCOND = ZERO RETURN END IF END IF * * Compute the norm of the matrix A. * ANORM = SLANST( '1', N, D, E ) * * Compute the reciprocal of the condition number of A. * CALL SPTCON( N, DF, EF, ANORM, RCOND, WORK, INFO ) * * Compute the solution vectors X. * CALL SLACPY( 'Full', N, NRHS, B, LDB, X, LDX ) CALL SPTTRS( N, NRHS, DF, EF, X, LDX, INFO ) * * Use iterative refinement to improve the computed solutions and * compute error bounds and backward error estimates for them. * CALL SPTRFS( N, NRHS, D, E, DF, EF, B, LDB, X, LDX, FERR, BERR, $ WORK, INFO ) * * Set INFO = N+1 if the matrix is singular to working precision. * IF( RCOND.LT.SLAMCH( 'Epsilon' ) ) $ INFO = N + 1 * RETURN * * End of SPTSVX * END