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6. la.ct

This section describes functions from file la.ct.

6.1 axpy

[y] = axpy(a,x)
 [y] = axpy(a,x) is the equivalent of y = y + a*x
   for scalar a and (real or complex) arrays x and y.
   axpy calls a fast BLAS-1 routine if available.
   Error codes:
   -1: Input/output arg y is not real/complex array
   -2: First input arg a is not a scalar
   -3: Second input arg x is not real/complex array
   -4: Second input arg x has size incompatible with y
   -5: If y is real a and x must also be real
   -6: If y is complex x must also be complex

6.2 chol

[B] = chol(A)
 B=chol(A) returns the Cholesky decomposition of a
   positive definite square matrix A: B'**B == A.
   A may be integer, real or complex valued.
   Only the upper half of A is referenced, the lower half
   is assumed to be Hermitian symmetric.
See also: linsolve, LU, matprod, det, eig, inv.
   Error codes:
   -1: Input arg is not a matrix
   -2: Input matrix is not square
   -3: Input matrix is not positive definite 

6.3 det

[d] = det(A)
 det(A) returns the determinant of a square matrix A.
   A may be integer, real or complex valued.
   If A is scalar, it is returned as such.
   Error codes:
   -1: Nonnumeric input arg
   -2: Input array is not a matrix
   -3: Input matrix is not square
   -4: Singular matrix 

6.4 eig

[D;V] = eig(A)
 eig(A) returns the eigenvalues of a square matrix A.
   [D,V] = eig(A) returns the eigenvalues in D and the
   right eigenvectors as columns of V. The eigenvectors
   satisfy A**V == D*V.
See also: inv, LU.
   Error codes:
   1: Failed to converge
   -1: Input arg is not an array
   -2: Input array is not a matrix
   -3: Input matrix is not square
   -4: Internal error
   

6.5 eye

[A] = eye(n)
 eye(n) returns the (integer) unit matrix of order n.
   n must be a non-negative scalar integer.
   eye(V) where V is a two-element integer vector with
   both elements equal and positive works also, thus
   you can also use eye(size(A)).
See also: ones, inv.
   Error codes:
   -1: Argument not an integer or IntArray
   -2: Negative dimension
   -3: IntArray rank not 1
   -4: IntArray length not 2
   -5: Integer vector elements are unequal
   

6.6 inv

[B] = inv(A)
 inv(A) returns the inverse of a square matrix A.
   A may be integer, real or complex valued.
   A may also be a scalar, in which case its reciprocal
   is returned.
See also: linsolve, LU, chol, matprod, det, eig.
   Error codes:
   -1: Nonnumeric input arg
   -2: Input array is not a matrix
   -3: Input matrix is not square
   -4: Singular matrix
   -5: Singular matrix 

6.7 linsolve

[x] = linsolve(A,b)
 linsolve(A,b) solves the linear system A**x == b.
   If A is square, the result x is roughly the same as
   computing inv(A)**b (however, using linsolve is
   faster and numerically more accurate). If A is not
   square, a least-square problem is solved. If the system
   is overdetermined, the solution x minimizes the quantity
   |A**x - b|. If the system is underdetermined, the
   solution x minimizes |x| among all x that satisfy
   A**x==b.
   The second argument may be a vector or a matrix.
   If it is a matrix, several linear systems are effectively
   solved simultaneously.
See also: inv, LU, eig, SVD.
   Error codes:
   -1: First input arg is not an array
   -2: First input arg is not a matrix
   -3: Second input arg is not an array
   -4: Second input arg is not a vector or matrix
   -5: Incompatible dimensions in first/second args
   -6: Matrix must be square
   -7: Singular matrix
   -8: Internal error
   

6.8 LU

[L;U,P] = LU(A)
 [L,U,P] = LU(A) computes the LU factorization of matrix A.
   The factorization is A = P**L**U, where P is a permutation
   matrix, L is lower triangular with unit diagonal and U is
   upper triangular.
   [lu] = LU(A) leaves the factors L and U packed in one matrix.
   [lu,p] = LU(A) returns also the pivoting info vector p.
   (Notice that this p is related to the permutation matrix P
   but is not the same. You need this form of LU if you want to
   use LUbacksubst later on.)
See also: LUbacksubst, linsolve, inv, chol, SVD.
   Error codes:
   1: Singular matrix (==> zero in U's diagonal)
   -1: Input arg not an array
   -2: Input arg not a rank-2 array (matrix)
   

6.9 LUbacksubst

[x] = LUbacksubst(lu,p,b)
 LUbacksubst(lu,p,b) solves the linear system A**x == b,
   where A has been previously LU-decomposed using LU:
   [lu,p] = LU(A).
See also: linsolve, LU.
   Error codes:
   -1: First input arg is not an array
   -2: First input arg is not a square matrix
   -3: Second input arg is not an integer vector
   -4: Third input arg is not an array
   -5: Third input arg is not a vector or matrix
   -6: Incompatible dimensions in first/third args
   -7: Singular matrix
   -8: Incompatible dimensions in first/second args
   -9: Internal error

6.10 matprod

[C] = matprod(A,B; Aflag,Bflag)
 matprod(A,B) returns the matrix product of A and B.
   If at least one of A and B is scalar, matprod(A,B) is the
   same as their ordinary product A*B. If both A and B
   are arrays, their "inner" dimensions must agree.
   That is, the last dimension of A must equal the first
   dimension of B.
   You can abbreviate matprod(A,B) as A**B.

   Optional args: matprod(A,B,aflag,bflag) can be used to
   transpose or Hermitian-conjugate the factors before the
   product. 'n' means no operation, 't' means transpose and
   'h' means Hermitian conjugate. For example,

   matprod(A,B,'h') = A'**B = herm(A)**B
   matprod(A,B,'n','t') = A**B.' = A**transpose(B)

   Normally you need not use matprod explicitly, but you
   can use the operator **, which is internally translated
   to matprod. Hermitian conjugates and transposes in
   connection with ** produce the corresponding 'h' and
   't' options in matprod. For example,

   A'**B        generates       matprod(A,B,'h')
   A.'**B'      generates       matprod(A,B,'t','h')
   A**B.'       generates       matprod(A,B,'n','t')

   and so on. The runtime is optimal for all these operations.
See also: inv.
   
   Error codes:
   -1: Inner dimensions do not agree
   -2: Resulting array would have too high rank
   -3: Third arg not one of 'n', 't', 'h'
   -4: Fourth arg not one of 'n', 't', 'h'
   

6.11 SVD

[U;S,V] = SVD(A)
 [U,S,V] = SVD(A) computes the singular value
   decomposition of matrix A: A = U**S**V'.
   U and V are unitary and S is diagonal.
   SVD(A) as such returns the vector of singular values.
See also: LU, chol.
   Error codes:
   1: No convergence
   -1: Input arg not an array
   -2: Input array is not a matrix
   -3: Internal error
   -4: Two output arg case not supported (must be 1 or 3)
   


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