clapack解超定線性方程組

有兩個網頁要重點參考，分别是：

網頁一：http://www.culatools.com/features/lapack/

網頁二：http://www.netlib.org/lapack/lug/node1.html

解超定線性方程組的其中一個函數如下：

SUBROUTINE DGELSD( M, N, NRHS, A, LDA, B, LDB, S, RCOND, RANK,
     $                   WORK, LWORK, IWORK, INFO )
*
*  -- LAPACK driver routine (version 3.2.2) --
*  -- LAPACK is a software package provided by Univ. of Tennessee,    --
*  -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
*     June 2010
*
*     .. Scalar Arguments ..
      INTEGER            INFO, LDA, LDB, LWORK, M, N, NRHS, RANK
      DOUBLE PRECISION   RCOND
*     ..
*     .. Array Arguments ..
      INTEGER            IWORK( * )
      DOUBLE PRECISION   A( LDA, * ), B( LDB, * ), S( * ), WORK( * )
*     ..
*
*  Purpose
*  =======
*
*  DGELSD computes the minimum-norm solution to a real linear least
*  squares problem:
*      minimize 2-norm(| b - A*x |)
*  using the singular value decomposition (SVD) of A. A is an M-by-N
*  matrix which may be rank-deficient.
*
*  Several right hand side vectors b and solution vectors x can be
*  handled in a single call; they are stored as the columns of the
*  M-by-NRHS right hand side matrix B and the N-by-NRHS solution
*  matrix X.
*
*  The problem is solved in three steps:
*  (1) Reduce the coefficient matrix A to bidiagonal form with
*      Householder transformations, reducing the original problem
*      into a "bidiagonal least squares problem" (BLS)
*  (2) Solve the BLS using a divide and conquer approach.
*  (3) Apply back all the Householder tranformations to solve
*      the original least squares problem.
*
*  The effective rank of A is determined by treating as zero those
*  singular values which are less than RCOND times the largest singular
*  value.
*
*  The divide and conquer algorithm makes very mild assumptions about
*  floating point arithmetic. It will work on machines with a guard
*  digit in add/subtract, or on those binary machines without guard
*  digits which subtract like the Cray X-MP, Cray Y-MP, Cray C-90, or
*  Cray-2. It could conceivably fail on hexadecimal or decimal machines
*  without guard digits, but we know of none.
*
*  Arguments
*  =========
*
*  M       (input) INTEGER
*          The number of rows of A. M >= 0.
*
*  N       (input) INTEGER
*          The number of columns of 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) DOUBLE PRECISION array, dimension (LDA,N)
*          On entry, the M-by-N matrix A.
*          On exit, A has been destroyed.
*
*  LDA     (input) INTEGER
*          The leading dimension of the array A.  LDA >= max(1,M).
*
*  B       (input/output) DOUBLE PRECISION array, dimension (LDB,NRHS)
*          On entry, the M-by-NRHS right hand side matrix B.
*          On exit, B is overwritten by the N-by-NRHS solution
*          matrix X.  If m >= n and RANK = n, the residual
*          sum-of-squares for the solution in the i-th column is given
*          by the sum of squares of elements n+1:m in that column.
*
*  LDB     (input) INTEGER
*          The leading dimension of the array B. LDB >= max(1,max(M,N)).
*
*  S       (output) DOUBLE PRECISION array, dimension (min(M,N))
*          The singular values of A in decreasing order.
*          The condition number of A in the 2-norm = S(1)/S(min(m,n)).
*
*  RCOND   (input) DOUBLE PRECISION
*          RCOND is used to determine the effective rank of A.
*          Singular values S(i) <= RCOND*S(1) are treated as zero.
*          If RCOND < 0, machine precision is used instead.
*
*  RANK    (output) INTEGER
*          The effective rank of A, i.e., the number of singular values
*          which are greater than RCOND*S(1).
*
*  WORK    (workspace/output) DOUBLE PRECISION array, dimension (MAX(1,LWORK))
*          On exit, if INFO = 0, WORK(1) returns the optimal LWORK.
*
*  LWORK   (input) INTEGER
*          The dimension of the array WORK. LWORK must be at least 1.
*          The exact minimum amount of workspace needed depends on M,
*          N and NRHS. As long as LWORK is at least
*              12*N + 2*N*SMLSIZ + 8*N*NLVL + N*NRHS + (SMLSIZ+1)**2,
*          if M is greater than or equal to N or
*              12*M + 2*M*SMLSIZ + 8*M*NLVL + M*NRHS + (SMLSIZ+1)**2,
*          if M is less than N, the code will execute correctly.
*          SMLSIZ is returned by ILAENV and is equal to the maximum
*          size of the subproblems at the bottom of the computation
*          tree (usually about 25), and
*             NLVL = MAX( 0, INT( LOG_2( MIN( M,N )/(SMLSIZ+1) ) ) + 1 )
*          For good performance, LWORK should generally be larger.
*
*          If LWORK = -1, then a workspace query is assumed; the routine
*          only calculates the optimal size of the WORK array, returns
*          this value as the first entry of the WORK array, and no error
*          message related to LWORK is issued by XERBLA.
*
*  IWORK   (workspace) INTEGER array, dimension (MAX(1,LIWORK))
*          LIWORK >= max(1, 3 * MINMN * NLVL + 11 * MINMN),
*          where MINMN = MIN( M,N ).
*          On exit, if INFO = 0, IWORK(1) returns the minimum LIWORK.
*
*  INFO    (output) INTEGER
*          = 0:  successful exit
*          < 0:  if INFO = -i, the i-th argument had an illegal value.
*          > 0:  the algorithm for computing the SVD failed to converge;
*                if INFO = i, i off-diagonal elements of an intermediate
*                bidiagonal form did not converge to zero.