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java.lang.Object | +----org.netlib.lapack.Dgelss
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. Contact seymour@cs.utk.edu with any questions.* .. * * Purpose * ======= * * DGELSS 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 effective rank of A is determined by treating as zero those * singular values which are less than RCOND times the largest singular * value. * * Arguments * ========= * * 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) DOUBLE PRECISION 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) 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 (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. * * 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 ..
Dgelss()
dgelss(int, int, int, double[], int, int, double[], int, int, double[], int, double, intW, double[], int, int, intW)
Dgelss
public Dgelss()
dgelss
public static void dgelss(int m,
int n,
int nrhs,
double a[],
int _a_offset,
int lda,
double b[],
int _b_offset,
int ldb,
double s[],
int _s_offset,
double rcond,
intW rank,
double work[],
int _work_offset,
int lwork,
intW info)
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