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Package matrix (in matrix.i) - LAPACK linear algebra functions

Index of documented functions or symbols:

LUrcond

DOCUMENT LUrcond(a)
      or LUrcond(a, one_norm=1)
  returns the reciprocal condition number of the N-by-N matrix A.
  If the ONE_NORM argument is non-nil and non-zero, the 1-norm
  condition number is returned, otherwise the infinity-norm condition
  number is returned.

  The condition number is the ratio of the largest to the smallest
  singular value, max(singular_values)*max(1/singular_values) (or
  sum(abs(singular_values)*sum(abs(1/singular_values)) if ONE_NORM
  is selected?).  If the reciprocal condition number is near zero
  then A is numerically singular; specifically, if
       1.0+LUrcond(a) == 1.0
  then A is numerically singular.

SEE ALSO: LUsolve

LUsolve

DOCUMENT LUsolve(a, b)
      or LUsolve(a, b, which=which)
      or a_inverse= LUsolve(a)

  returns the solution x of the matrix equation:
     A(,+)*x(+) = B
  If A is an n-by-n matrix then B must have length n, and the returned
  x will also have length n.

  B may have additional dimensions, in which case the returned x
  will have the same additional dimensions.  The WHICH dimension of B,
  and of the returned x is the one of length n which participates
  in the matrix solve.  By default, WHICH=1, so that the equations
  being solved are:
     A(,+)*x(+,..) = B
  Non-positive WHICH counts from the final dimension (as for the
  sort and transpose functions), so that WHICH=0 solves:
     x(..,+)*A(,+) = B
  Other examples:
     A_ij X_jklm = B_iklm   (WHICH=1)
     A_ij X_kjlm = B_kilm   (WHICH=2)
     A_ij X_klmj = B_klmi   (WHICH=4 or WHICH=0)

  If the B argument is omitted, the inverse of A is returned:
  A(,+)*x(+,) and A(,+)*x(,+) will be unit matrices.

  LUsolve works by LU decomposition using Gaussian elimination with
  pivoting.  It is the fastest way to solve square matrices.  QRsolve
  handles non-square matrices, as does SVsolve.  SVsolve is slowest,
  but can deal with highly singular matrices sensibly.

SEE ALSO: QRsolve, TDsolve, SVsolve, SVdec, LUrcond

QRsolve

DOCUMENT QRsolve(a, b)
      or QRsolve(a, b, which=which)

  returns the solution x (in a least squares or least norm sense
  described below) of the matrix equation:
     A(,+)*x(+) = B
  If A is an m-by-n matrix (i.e.- m equations in n unknowns), then B
  must have length m, and the returned x will have length n.

  If nm, the system is underdetermined -- many solutions are possible
          -- the returned x has minimum L2 norm among all solutions

  B may have additional dimensions, in which case the returned x
  will have the same additional dimensions also have those dimensions.
  The WHICH dimension of B and the returned x is the one of length m
  or n which participates in the matrix solve.  By default, WHICH=1,
  so that the equations being solved are:
     A(,+)*x(+,..) = B
  Non-positive WHICH counts from the final dimension (as for the
  sort and transpose functions), so that WHICH=0 solves:
     A(,+)*x(..,+) = B

  QRsolve works by QR factorization if nm.
  QRsolve is slower than LUsolve.  Its main attraction is that it can
  handle overdetermined or underdetermined systems of equations
  (nonsquare matrices).  QRsolve may fail for singular systems; try
  SVsolve in this case.

SEE ALSO: LUsolve, TDsolve, SVsolve, SVdec

SVdec

DOCUMENT s= SVdec(a, u, vt)
      or s= SVdec(a, u, vt, full=1)

  performs the singular value decomposition of the m-by-n matrix A:
     A = (U(,+) * SIGMA(+,))(,+) * VT(+,)
  where U is an m-by-m orthogonal matrix, VT is an n-by-n orthogonal
  matrix, and SIGMA is an m-by-n matrix which is zero except for its
  min(m,n) diagonal elements.  These diagonal elements are the return
  value of the function, S.  The returned S is always arranged in
  order of descending absolute value.  U(,1:min(m,n)) are the left
  singular vectors corresponding to the min(m,n) elements of S;
  VT(1:min(m,n),) are the right singular vectors.  (The original A
  matrix maps a right singular vector onto the corresponding left
  singular vector, stretched by a factor of the singular value.)

  Note that U and VT are strictly outputs; if you don't need them,
  they need not be present in the calling sequence.

  By default, U will be an m-by-min(m,n) matrix, and V will be
  a min(m,n)-by-n matrix (i.e.- only the singular vextors are returned,
  not the full orthogonal matrices).  Set the FULL keyword to a
  non-zero value to get the full m-by-m and n-by-n matrices.

  On rare occasions, the routine may fail; if it does, the
  first SVinfo values of the returned S are incorrect.  Hence,
  the external variable SVinfo will be 0 after a successful call
  to SVdec.  If SVinfo>0, then external SVe contains the superdiagonal
  elements of the bidiagonal matrix whose diagonal is the returned
  S, and that bidiagonal matrix is equal to (U(+,)*A(+,))(,+) * V(+,).

  Numerical Recipes (Press, et. al. Cambridge University Press 1988)
  has a good discussion of how to use the SVD -- see section 2.9.

SEE ALSO: SVsolve, LUsolve, QRsolve, TDsolve

SVsolve

DOCUMENT SVsolve(a, b)
      or SVsolve(a, b, rcond)
      or SVsolve(a, b, rcond, which=which)

  returns the solution x (in a least squares sense described below) of
  the matrix equation:
     A(,+)*x(+) = B
  If A is an m-by-n matrix (i.e.- m equations in n unknowns), then B
  must have length m, and the returned x will have length n.

  If nm, the system is underdetermined -- many solutions are possible
          -- the returned x has minimum L2 norm among all solutions

  SVsolve works by singular value decomposition, therefore it is
  immune to failure due to singularity of the A matrix.  The optional
  RCOND argument defaults to 1.0e-9; singular values less than RCOND
  times the largest singular value (absolute value) will be set to 0.0.
  If RCOND<=0.0, machine precision is used.  The effective rank of the
  matrix is returned as the external variable SVrank.

  You can examine the details of the SVD by calling the SVdec routine,
  which returns the singular vectors as well as the singular values.
  Numerical Recipes (Press, et. al. Cambridge University Press 1988)
  has a good discussion of how to use the SVD -- see section 2.9.

  B may have additional dimensions, in which case the returned x
  will have the same additional dimensions.  The WHICH argument
  (default 1) controls which dimension of B takes part in the matrix
  solve.  See QRsolve or LUsolve for a complete discussion.

SEE ALSO: SVdec, LUsolve, QRsolve, TDsolve

TDsolve

DOCUMENT TDsolve(c, d, e, b)
      or TDsolve(c, d, e, b, which=which)

  returns the solution to the tridiagonal system:
     D(1)*x(1)       + E(1)*x(2)                       = B(1)
     C(1:-1)*x(1:-2) + D(2:-1)*x(2:-1) + E(2:0)*x(3:0) = B(2:-1)
                       C(0)*x(-1)      + D(0)*x(0)     = B(0)
  (i.e.- C is the subdiagonal, D the diagonal, and E the superdiagonal;
  C and E have one fewer element than D, which is the same length as
  both B and x)

  B may have additional dimensions, in which case the returned x
  will have the same additional dimensions.  The WHICH dimension of B,
  and of the returned x is the one of length n which participates
  in the matrix solve.  By default, WHICH=1, so that the equations
  being solved involve B(,..) and x(+,..).
  Non-positive WHICH counts from the final dimension (as for the
  sort and transpose functions), so that WHICH=0 involves B(..,)
  and x(..,+).

  The C, D, and E arguments may be either scalars or vectors; they
  will be broadcast as appropriate.

SEE ALSO: LUsolve, QRsolve, SVsolve, SVdec

unit

DOCUMENT unit(n)
      or unit(n, m)
  returns n-by-n (or n-by-m) unit matrix, i.e.- matrix with diagonal
  elements all 1.0, off diagonal elements 0.0