public class Slalsd
- extends java.lang.Object
Following is the description from the original
Fortran source. For each array argument, the Java
version will include an integer offset parameter, so
the arguments may not match the description exactly.
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* SLALSD uses the singular value decomposition of A to solve the least
* squares problem of finding X to minimize the Euclidean norm of each
* column of A*X-B, where A is N-by-N upper bidiagonal, and X and B
* are N-by-NRHS. The solution X overwrites B.
* The singular values of A smaller than RCOND times the largest
* singular value are treated as zero in solving the least squares
* problem; in this case a minimum norm solution is returned.
* The actual singular values are returned in D in ascending order.
* This code 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 XMP, Cray YMP, Cray C 90, or Cray 2.
* It could conceivably fail on hexadecimal or decimal machines
* without guard digits, but we know of none.
* UPLO (input) CHARACTER*1
* = 'U': D and E define an upper bidiagonal matrix.
* = 'L': D and E define a lower bidiagonal matrix.
* SMLSIZ (input) INTEGER
* The maximum size of the subproblems at the bottom of the
* computation tree.
* N (input) INTEGER
* The dimension of the bidiagonal matrix. N >= 0.
* NRHS (input) INTEGER
* The number of columns of B. NRHS must be at least 1.
* D (input/output) REAL array, dimension (N)
* On entry D contains the main diagonal of the bidiagonal
* matrix. On exit, if INFO = 0, D contains its singular values.
* E (input) REAL array, dimension (N-1)
* Contains the super-diagonal entries of the bidiagonal matrix.
* On exit, E has been destroyed.
* B (input/output) REAL array, dimension (LDB,NRHS)
* On input, B contains the right hand sides of the least
* squares problem. On output, B contains the solution X.
* LDB (input) INTEGER
* The leading dimension of B in the calling subprogram.
* LDB must be at least max(1,N).
* RCOND (input) REAL
* The singular values of A less than or equal to RCOND times
* the largest singular value are treated as zero in solving
* the least squares problem. If RCOND is negative,
* machine precision is used instead.
* For example, if diag(S)*X=B were the least squares problem,
* where diag(S) is a diagonal matrix of singular values, the
* solution would be X(i) = B(i) / S(i) if S(i) is greater than
* RCOND*max(S), and X(i) = 0 if S(i) is less than or equal to
* RANK (output) INTEGER
* The number of singular values of A greater than RCOND times
* the largest singular value.
* WORK (workspace) REAL array, dimension at least
* (9*N + 2*N*SMLSIZ + 8*N*NLVL + N*NRHS + (SMLSIZ+1)**2),
* where NLVL = max(0, INT(log_2 (N/(SMLSIZ+1))) + 1).
* IWORK (workspace) INTEGER array, dimension at least
* (3*N*NLVL + 11*N)
* INFO (output) INTEGER
* = 0: successful exit.
* < 0: if INFO = -i, the i-th argument had an illegal value.
* > 0: The algorithm failed to compute an singular value while
* working on the submatrix lying in rows and columns
* INFO/(N+1) through MOD(INFO,N+1).
* Further Details
* Based on contributions by
* Ming Gu and Ren-Cang Li, Computer Science Division, University of
* California at Berkeley, USA
* Osni Marques, LBNL/NERSC, USA
* .. Parameters ..
|Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
public static void slalsd(java.lang.String uplo,