SRC/dorcsd.f(3)

📦
version 3.12.1 · 7 versions
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💖 NAME 💖

okay bestie, welcome to SRC/dorcsd.f — this page is from the Library Functions Manual, and it's all about a recursive subroutine named dorcsd (aka DORCSD). It's giving linear algebra main character energy. 💅

✨ SYNOPSIS ✨

🎀 Functions/Subroutines 🎀

  • recursive subroutine dorcsd (jobu1, jobu2, jobv1t, jobv2t, trans, signs, m, p, q, x11, ldx11, x12, ldx12, x21, ldx21, x22, ldx22, theta, u1, ldu1, u2, ldu2, v1t, ldv1t, v2t, ldv2t, work, lwork, iwork, info) — DORCSD

🌸 Function/Subroutine Documentation 🌸

🦋 recursive subroutine dorcsd 🦋

Here's the full typed-out signature, no cap:

recursive subroutine dorcsd (character jobu1, character jobu2, character jobv1t, character jobv2t, character trans, character signs, integer m, integer p, integer q, double precision, dimension( ldx11, * ) x11, integer ldx11, double precision, dimension( ldx12, * ) x12, integer ldx12, double precision, dimension( ldx21, * ) x21, integer ldx21, double precision, dimension( ldx22, * ) x22, integer ldx22, double precision, dimension( * ) theta, double precision, dimension( ldu1, * ) u1, integer ldu1, double precision, dimension( ldu2, * ) u2, integer ldu2, double precision, dimension( ldv1t, * ) v1t, integer ldv1t, double precision, dimension( ldv2t, * ) v2t, integer ldv2t, double precision, dimension( * ) work, integer lwork, integer, dimension( * ) iwork, integer info)

and the one-liner is simply DORCSD.

🔥 Purpose 🔥

DORCSD computes the CS decomposition of an M-by-M partitioned orthogonal matrix X. The docs serve this exact diagram:

                                [  I  0  0 |  0  0  0 ]
                                [  0  C  0 |  0 -S  0 ]
    [ X11 | X12 ]   [ U1 |    ] [  0  0  0 |  0  0 -I ] [ V1 |    ]**T
X = [-----------] = [---------] [---------------------] [---------]   .
    [ X21 | X22 ]   [    | U2 ] [  0  0  0 |  I  0  0 ] [    | V2 ]
                                [  0  S  0 |  0  C  0 ]
                                [  0  0  I |  0  0  0 ]

X11 is P-by-Q. The orthogonal matrices U1, U2, V1, and V2 are P-by-P, (M-P)-by-(M-P), Q-by-Q, and (M-Q)-by-(M-Q), respectively. C and S are R-by-R nonnegative diagonal matrices satisfying C^2 + S^2 = I, in which R = MIN(P,M-P,Q,M-Q).

💜 Parameters 💜

🍓 JOBU1

  • JOBU1 is CHARACTER
  • = 'Y': U1 is computed;
  • otherwise: U1 is not computed.

🍓 JOBU2

  • JOBU2 is CHARACTER
  • = 'Y': U2 is computed;
  • otherwise: U2 is not computed.

🍓 JOBV1T

  • JOBV1T is CHARACTER
  • = 'Y': V1T is computed;
  • otherwise: V1T is not computed.

🍓 JOBV2T

  • JOBV2T is CHARACTER
  • = 'Y': V2T is computed;
  • otherwise: V2T is not computed.

🍓 TRANS

  • TRANS is CHARACTER
  • = 'T': X, U1, U2, V1T, and V2T are stored in row-major order;
  • otherwise: X, U1, U2, V1T, and V2T are stored in column-major order.

🍓 SIGNS

  • SIGNS is CHARACTER
  • = 'O': the lower-left block is made nonpositive (the convention);
  • otherwise: the upper-right block is made nonpositive (the convention).

🍓 M

  • M is INTEGER
  • The number of rows and columns in X.

🍓 P

  • P is INTEGER
  • The number of rows in X11 and X12. 0 <= P <= M.

🍓 Q

  • Q is INTEGER
  • The number of columns in X11 and X21. 0 <= Q <= M.

🍓 X11

  • X11 is DOUBLE PRECISION array, dimension (LDX11,Q)
  • On entry, part of the orthogonal matrix whose CSD is desired.

🍓 LDX11

  • LDX11 is INTEGER
  • The leading dimension of X11. LDX11 >= MAX(1,P).

🍓 X12

  • X12 is DOUBLE PRECISION array, dimension (LDX12,M-Q)
  • On entry, part of the orthogonal matrix whose CSD is desired.

🍓 LDX12

  • LDX12 is INTEGER
  • The leading dimension of X12. LDX12 >= MAX(1,P).

🍓 X21

  • X21 is DOUBLE PRECISION array, dimension (LDX21,Q)
  • On entry, part of the orthogonal matrix whose CSD is desired.

🍓 LDX21

  • LDX21 is INTEGER
  • The leading dimension of X11. LDX21 >= MAX(1,M-P).

🍓 X22

  • X22 is DOUBLE PRECISION array, dimension (LDX22,M-Q)
  • On entry, part of the orthogonal matrix whose CSD is desired.

🍓 LDX22

  • LDX22 is INTEGER
  • The leading dimension of X11. LDX22 >= MAX(1,M-P).

🍓 THETA

  • THETA is DOUBLE PRECISION array, dimension (R), in which R = MIN(P,M-P,Q,M-Q).
  • C = DIAG( COS(THETA(1)), ... , COS(THETA(R)) ) and
  • S = DIAG( SIN(THETA(1)), ... , SIN(THETA(R)) ).

🍓 U1

  • U1 is DOUBLE PRECISION array, dimension (LDU1,P)
  • If JOBU1 = 'Y', U1 contains the P-by-P orthogonal matrix U1.

🍓 LDU1

  • LDU1 is INTEGER
  • The leading dimension of U1. If JOBU1 = 'Y', LDU1 >= MAX(1,P).

🍓 U2

  • U2 is DOUBLE PRECISION array, dimension (LDU2,M-P)
  • If JOBU2 = 'Y', U2 contains the (M-P)-by-(M-P) orthogonal matrix U2.

🍓 LDU2

  • LDU2 is INTEGER
  • The leading dimension of U2. If JOBU2 = 'Y', LDU2 >= MAX(1,M-P).

🍓 V1T

  • V1T is DOUBLE PRECISION array, dimension (LDV1T,Q)
  • If JOBV1T = 'Y', V1T contains the Q-by-Q matrix orthogonal matrix V1**T.

🍓 LDV1T

  • LDV1T is INTEGER
  • The leading dimension of V1T. If JOBV1T = 'Y', LDV1T >= MAX(1,Q).

🍓 V2T

  • V2T is DOUBLE PRECISION array, dimension (LDV2T,M-Q)
  • If JOBV2T = 'Y', V2T contains the (M-Q)-by-(M-Q) orthogonal matrix V2**T.

🍓 LDV2T

  • LDV2T is INTEGER
  • The leading dimension of V2T. If JOBV2T = 'Y', LDV2T >= MAX(1,M-Q).

🍓 WORK

  • WORK is DOUBLE PRECISION array, dimension (MAX(1,LWORK))
  • On exit, if INFO = 0, WORK(1) returns the optimal LWORK.
  • If INFO > 0 on exit, WORK(2:R) contains the values PHI(1), ..., PHI(R-1) that, together with THETA(1), ..., THETA(R), define the matrix in intermediate bidiagonal-block form remaining after nonconvergence. INFO specifies the number of nonzero PHI's.

🍓 LWORK

  • LWORK is INTEGER
  • The dimension of the array WORK.
  • 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

  • IWORK is INTEGER array, dimension (M-MIN(P, M-P, Q, M-Q))

🍓 INFO

  • INFO is INTEGER
  • = 0: successful exit.
  • < 0: if INFO = -i, the i-th argument had an illegal value.
  • > 0: DBBCSD did not converge. See the description of WORK above for details.

🌈 References 🌈

[1] Brian D. Sutton. Computing the complete CS decomposition. Numer. Algorithms, 50(1):33-65, 2009.

👑 Author 👑

  • Univ. of Tennessee
  • Univ. of California Berkeley
  • Univ. of Colorado Denver
  • NAG Ltd.

📌 Definition 📌

  • Definition at line 295 of file dorcsd.f.

👑 Author 👑

  • Generated automatically by Doxygen for LAPACK from the source code.

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