MD03BX - SLICOT Library Routine Documentation

## MD03BX

### QR factorization with column pivoting for a standard nonlinear least squares problem

[Specification] [Arguments] [Method] [References] [Comments] [Example]

Purpose

      To compute the QR factorization with column pivoting of an
      m-by-n matrix J (m >= n), that is, J*P = Q*R, where Q is a matrix
      with orthogonal columns, P a permutation matrix, and R an upper
      trapezoidal matrix with diagonal elements of nonincreasing
      magnitude, and to apply the transformation Q' on the error
      vector e (in-situ). The 1-norm of the scaled gradient is also
      returned. The matrix J could be the Jacobian of a nonlinear least
      squares problem.

Specification

          SUBROUTINE MD03BX( M, N, FNORM, J, LDJ, E, JNORMS, GNORM, IPVT,
         $                   DWORK, LDWORK, INFO )
    C     .. Scalar Arguments ..
          INTEGER           INFO, LDJ, LDWORK, M, N
          DOUBLE PRECISION  FNORM, GNORM
    C     .. Array Arguments ..
          INTEGER           IPVT(*)
          DOUBLE PRECISION  DWORK(*), E(*), J(*), JNORMS(*)

Arguments

Input/Output Parameters

      M       (input) INTEGER
              The number of rows of the Jacobian matrix J.  M >= 0.

      N       (input) INTEGER
              The number of columns of the Jacobian matrix J.
              M >= N >= 0.

      FNORM   (input) DOUBLE PRECISION
              The Euclidean norm of the vector e.  FNORM >= 0.

      J       (input/output) DOUBLE PRECISION array, dimension (LDJ, N)
              On entry, the leading M-by-N part of this array must
              contain the Jacobian matrix J.
              On exit, the leading N-by-N upper triangular part of this
              array contains the upper triangular factor R of the
              Jacobian matrix. Note that for efficiency of the later
              calculations, the matrix R is delivered with the leading
              dimension MAX(1,N), possibly much smaller than the value
              of LDJ on entry.

      LDJ     (input/output) INTEGER
              The leading dimension of array J.
              On entry, LDJ >= MAX(1,M).
              On exit,  LDJ >= MAX(1,N).

      E       (input/output) DOUBLE PRECISION array, dimension (M)
              On entry, this array must contain the error vector e.
              On exit, this array contains the updated vector Q'*e.

      JNORMS  (output) DOUBLE PRECISION array, dimension (N)
              This array contains the Euclidean norms of the columns of
              the Jacobian matrix, considered in the initial order.

      GNORM   (output) DOUBLE PRECISION
              If FNORM > 0, the 1-norm of the scaled vector
              J'*Q'*e/FNORM, with each element i further divided by
              JNORMS(i) (if JNORMS(i) is nonzero).
              If FNORM = 0, the returned value of GNORM is 0.

      IPVT    (output) INTEGER array, dimension (N)
              This array defines the permutation matrix P such that
              J*P = Q*R. Column j of P is column IPVT(j) of the identity
              matrix.

Workspace

      DWORK   DOUBLE PRECISION array, dimension (LDWORK)
              On exit, if INFO = 0, DWORK(1) returns the optimal value
              of LDWORK.

      LDWORK  INTEGER
              The length of the array DWORK.
              LDWORK >= 1,      if N = 0 or M = 1;
              LDWORK >= 4*N+1,  if N > 1.
              For optimum performance LDWORK should be larger.

Error Indicator

      INFO    INTEGER
              = 0:  successful exit;
              < 0:  if INFO = -i, the i-th argument had an illegal
                    value.

Method

      The algorithm uses QR factorization with column pivoting of the
      matrix J, J*P = Q*R, and applies the orthogonal matrix Q' to the
      vector e.

Further Comments

      None

Example

Program Text

      None

Program Data

      None

Program Results

      None

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