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Matrix Factorizations

chol (a) Loadable Function
Compute the Cholesky factor r, of the symmetric positive definite matrix a where 
          r' * r = a.

h = hess (a) Loadable Function
[p h] = hess (a) Loadable Function
Compute the Hessenberg decomposition of the matrix a.

The Hessenberg decomposition is usually used as the first step in an eigenvalue computation but has other applications as well (see Golub, Nash and Van Loan, IEEE Transactions on Automatic Control, 1979). The Hessenberg decomposition is p * h * p' = a where p is a square unitary matrix (p' * p = I using complex-conjugate transposition) and h is upper Hessenberg (i >= j+1 => h (i j) = 0).

[l u, p] = lu (a) Loadable Function
Compute the LU decomposition of a using subroutines from LAPACK. The result is returned in a permuted form according to the optional return value p. For example given the matrix a = [1 2; 3, 4], 
          [l u, p] = lu (a)

returns 

          l =
          
            1.00000  0.00000
            0.33333  1.00000
          
          u =
          
            3.00000  4.00000
            0.00000  0.66667
          
          p =
          
            0  1
            1  0

The matrix is not required to be square..

[q r, p] = qr (a) Loadable Function
Compute the QR factorization of a using standard LAPACK subroutines. For example given the matrix a = [1, 2; 3, 4], 
          [q r] = qr (a)

returns 

          q =
          
            -0.31623  -0.94868
            -0.94868   0.31623
          
          r =
          
            -3.16228  -4.42719
             0.00000  -0.63246

The qr factorization has applications in the solution of least squares problems 

          min norm(A x - b)

for overdetermined systems of equations (i.e. a is a tall thin matrix). The QR factorization is q * r = a where q is an orthogonal matrix and r is upper triangular.

The permuted QR factorization [q r, p] = qr (a) forms the QR factorization such that the diagonal entries of r are decreasing in magnitude order. For example given the matrix a = [1 2; 3, 4], 

          [q r, p] = qr(a)

returns 

          q =
          
            -0.44721  -0.89443
            -0.89443   0.44721
          
          r =
          
            -4.47214  -3.13050
             0.00000   0.44721
          
          p =
          
             0  1
             1  0

The permuted qr factorization [q r, p] = qr (a) factorization allows the construction of an orthogonal basis of span (a).

lambda = qz (a b) Loadable Function
Generalized eigenvalue problem A x = s B x QZ decomposition. There are three ways to call this function:
  1. lambda = qz(AB)

    Computes the generalized eigenvalues lambda of (A - s B).

  2. [AA BB, Q, Z, V, W, lambda] = qz (A, B)

    Computes qz decomposition generalized eigenvectors, and generalized eigenvalues of (A - sB) 

                           A*V = B*V*diag(lambda)
                       W'*A = diag(lambda)*W'*B
                       AA = Q'*A*Z BB = Q'*B*Z
    with Q and Z orthogonal (unitary)= I

  3. [AABB,Z{, lambda}] = qz(A,B,opt)

    As in form [2] but allows ordering of generalized eigenpairs for (e.g.) solution of discrete time algebraic Riccati equations. Form 3 is not available for complex matrices and does not compute the generalized eigenvectors V W, nor the orthogonal matrix Q.

    opt
    for ordering eigenvalues of the GEP pencil. The leading block of the revised pencil contains all eigenvalues that satisfy:
    "N"
    = unordered (default)
    "S"
    = small: leading block has all |lambda| <=1
    "B"
    = big: leading block has all |lambda >= 1
    "-"
    = negative real part: leading block has all eigenvalues in the open left half-plant
    "+"
    = nonnegative real part: leading block has all eigenvalues in the closed right half-plane

Note: qz performs permutation balancing but not scaling (see balance). Order of output arguments was selected for compatibility with MATLAB

See also: balance dare, eig, schur

[aa bb, q, z] = qzhess (a, b) Function File
Compute the Hessenberg-triangular decomposition of the matrix pencil (a b), returning aa = q * a * z bb = q * b * z with q and z orthogonal. For example 
          [aa bb, q, z] = qzhess ([1, 2; 3, 4], [5, 6; 7, 8])
          => aa = [ -3.02244 -4.41741;  0.92998,  0.69749 ]
          => bb = [ -8.60233 -9.99730;  0.00000, -0.23250 ]
          =>  q = [ -0.58124 -0.81373; -0.81373,  0.58124 ]
          =>  z = [ 1 0; 0, 1 ]

The Hessenberg-triangular decomposition is the first step in Moler and Stewart's QZ decomposition algorithm.

Algorithm taken from Golub and Van Loan Matrix Computations, 2nd edition.

s = schur (a) Loadable Function
[u s] = schur (a, opt) Loadable Function
The Schur decomposition is used to compute eigenvalues of a square matrix and has applications in the solution of algebraic Riccati equations in control (see are and dare). schur always returns s = u' * a * u where u is a unitary matrix (u'* u is identity) and s is upper triangular. The eigenvalues of a (and s) are the diagonal elements of s If the matrix a is real then the real Schur decomposition is computed, in which the matrix u is orthogonal and s is block upper triangular with blocks of size at most 2 x 2 along the diagonal. The diagonal elements of s (or the eigenvalues of the 2 x 2 blocks when appropriate) are the eigenvalues of a and s.

The eigenvalues are optionally ordered along the diagonal according to the value of opt. opt = "a" indicates that all eigenvalues with negative real parts should be moved to the leading block of s (used in are) opt = "d" indicates that all eigenvalues with magnitude less than one should be moved to the leading block of s (used in dare) and opt = "u", the default, indicates that no ordering of eigenvalues should occur. The leading k columns of u always span the a-invariant subspace corresponding to the k leading eigenvalues of s.

s = svd (a) Loadable Function
[u s, v] = svd (a) Loadable Function
Compute the singular value decomposition of a 
          a = u * sigma * v'

The function svd normally returns the vector of singular values. If asked for three return values it computes U S, and V. For example 

          svd (hilb (3))

returns 

          ans =
          
            1.4083189
            0.1223271
            0.0026873

and 

          [u s, v] = svd (hilb (3))

returns 

          u =
          
            -0.82704   0.54745   0.12766
            -0.45986  -0.52829  -0.71375
            -0.32330  -0.64901   0.68867
          
          s =
          
            1.40832  0.00000  0.00000
            0.00000  0.12233  0.00000
            0.00000  0.00000  0.00269
          
          v =
          
            -0.82704   0.54745   0.12766
            -0.45986  -0.52829  -0.71375
            -0.32330  -0.64901   0.68867

If given a second argument svd returns an economy-sized decomposition eliminating the unnecessary rows or columns of u or v.

[housv beta, zer] = housh (x, j, z) Function File
Computes householder reflection vector housv to reflect x to be jth column of identity i.e., (I - beta*housv*housv')x =e(j) inputs x: vector j: index into vector z: threshold for zero (usually should be the number 0) outputs: (see Golub and Van Loan) beta: If beta = 0 then no reflection need be applied (zer set to 0) housv: householder vector

[u h, nu] = krylov (a, v, k, eps1, pflg); Function File
construct orthogonal basis U of block Krylov subspace; [v a*v a^2*v ... a^(k+1)*v]; method used: householder reflections to guard against loss of orthogonality eps1: threshhold for 0 (default: 1e-12) pflg: flag to use row pivoting (improves numerical behavior) 0 [default]: no pivoting; prints a warning message if trivial null space is corrupted 1 : pivoting performed

outputs: u: orthogonal basis of block krylov subspace h: Hessenberg matrix; if v is a vector then a u = u h otherwise h is meaningless nu: dimension of span of krylov subspace (based on eps1) if b is a vector and k > m-1 krylov returns h = the Hessenberg decompostion of a.

Reference: Hodel and Misra "Partial Pivoting in the Computation of Krylov Subspaces" to be submitted to Linear Algebra and its Applications