public class SGELSS
- extends java.lang.Object
SGELSS is a simplified interface to the JLAPACK routine sgelss.
This interface converts Java-style 2D row-major arrays into
the 1D column-major linearized arrays expected by the lower
level JLAPACK routines. Using this interface also allows you
to omit offset and leading dimension arguments. However, because
of these conversions, these routines will be slower than the low
level ones. Following is the description from the original Fortran
source. Contact firstname.lastname@example.org with any questions.
* SGELSS 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
* The effective rank of A is determined by treating as zero those
* singular values which are less than RCOND times the largest singular
* M (input) INTEGER
* The number of rows of the matrix A. M >= 0.
* N (input) INTEGER
* The number of columns 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) REAL array, dimension (LDA,N)
* On entry, the M-by-N matrix A.
* On exit, the first min(m,n) rows of A are overwritten with
* its right singular vectors, stored rowwise.
* LDA (input) INTEGER
* The leading dimension of the array A. LDA >= max(1,M).
* B (input/output) REAL 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) REAL 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) REAL
* 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) REAL array, dimension (LWORK)
* On exit, if INFO = 0, WORK(1) returns the optimal LWORK.
* LWORK (input) INTEGER
* The dimension of the array WORK. LWORK >= 1, and also:
* LWORK >= 3*min(M,N) + max( 2*min(M,N), max(M,N), NRHS )
* 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.
* 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.
* .. Parameters ..
|Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
public static void SGELSS(int m,