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Compute the Cholesky factor, R, of the symmetric positive definite matrix A.
The Cholesky factor is defined by
R' * R = A.
Called with one output argument chol fails if A or S is
not positive definite. With two or more output arguments p flags
whether the matrix was positive definite and chol does not fail. A
zero value indicated that the matrix was positive definite and the R
gives the factorization, and p will have a positive value otherwise.
If called with 3 outputs then a sparsity preserving row/column permutation
is applied to A prior to the factorization. That is R is the
factorization of A(Q,Q) such that
R' * R = Q' * A * Q.
The sparsity preserving permutation is generally returned as a matrix.
However, given the flag "vector", Q will be returned as a
vector such that
R' * R = A(Q, Q).
Called with either a sparse or full matrix and using the "lower"
flag, chol returns the lower triangular factorization such that
L * L' = A.
For full matrices, if the "lower" flag is set only the lower
triangular part of the matrix is used for the factorization, otherwise the
upper triangular part is used.
In general the lower triangular factorization is significantly faster for sparse matrices.
See also: hess, lu, qr, qz, schur, svd, ichol, cholinv, chol2inv, cholupdate, cholinsert, choldelete, cholshift.
Compute the inverse of the symmetric positive definite matrix A using the Cholesky factorization.
Invert a symmetric, positive definite square matrix from its Cholesky decomposition, U.
Note that U should be an upper-triangular matrix with positive
diagonal elements. chol2inv (U) provides
inv (U'*U) but it is much faster than using inv.
Update or downdate a Cholesky factorization.
Given an upper triangular matrix R and a column vector u, attempt to determine another upper triangular matrix R1 such that
"+"
"-"
If op is "-", info is set to
If info is not present, an error message is printed in cases 1 and 2.
See also: chol, cholinsert, choldelete, cholshift.
Given a Cholesky factorization of a real symmetric or complex Hermitian positive definite matrix A = R’*R, R upper triangular, return the Cholesky factorization of A1, where A1(p,p) = A, A1(:,j) = A1(j,:)’ = u and p = [1:j-1,j+1:n+1]. u(j) should be positive.
On return, info is set to
If info is not present, an error message is printed in cases 1 and 2.
See also: chol, cholupdate, choldelete, cholshift.
Given a Cholesky factorization of a real symmetric or complex Hermitian positive definite matrix A = R’*R, R upper triangular, return the Cholesky factorization of A(p,p), where p = [1:j-1,j+1:n+1].
See also: chol, cholupdate, cholinsert, cholshift.
Given a Cholesky factorization of a real symmetric or complex Hermitian
positive definite matrix A = R’*R, R upper
triangular, return the Cholesky factorization of
A(p,p), where p is the permutation
p = [1:i-1, shift(i:j, 1), j+1:n] if i < j
or
p = [1:j-1, shift(j:i,-1), i+1:n] if j < i.
See also: chol, cholupdate, cholinsert, choldelete.
Compute the Hessenberg decomposition of the matrix A.
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
(H(i, j) = 0 forall i > j+1).
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).
Compute the LU decomposition of A.
If A is full subroutines from LAPACK are used and if A is sparse then UMFPACK is used.
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.
When called with two or three output arguments and a spare input matrix,
lu does not attempt to perform sparsity preserving column
permutations. Called with a fourth output argument, the sparsity
preserving column transformation Q is returned, such that
P * A * Q = L * U.
Called with a fifth output argument and a sparse input matrix,
lu attempts to use a scaling factor R on the input matrix
such that
P * (R \ A) * Q = L * U.
This typically leads to a sparser and more stable factorization.
An additional input argument thres, that defines the pivoting
threshold can be given. thres can be a scalar, in which case
it defines the UMFPACK pivoting tolerance for both symmetric and
unsymmetric cases. If thres is a 2-element vector, then the first
element defines the pivoting tolerance for the unsymmetric UMFPACK
pivoting strategy and the second for the symmetric strategy. By default,
the values defined by spparms are used ([0.1, 0.001]).
Given the string argument "vector", lu returns the values
of P and Q as vector values, such that for full matrix,
A (P,:) = L * U, and R(P,:)
* A (:, Q) = L * U.
With two output arguments, returns the permuted forms of the upper and
lower triangular matrices, such that A = L * U.
With one output argument y, then the matrix returned by the LAPACK
routines is returned. If the input matrix is sparse then the matrix L
is embedded into U to give a return value similar to the full case.
For both full and sparse matrices, lu loses the permutation
information.
Given an LU factorization of a real or complex matrix A = L*U, L lower unit trapezoidal and U upper trapezoidal, return the LU factorization of A + x*y.’, where x and y are column vectors (rank-1 update) or matrices with equal number of columns (rank-k update).
Optionally, row-pivoted updating can be used by supplying a row permutation
(pivoting) matrix P; in that case, an updated permutation matrix is
returned. Note that if L, U, P is a pivoted
LU factorization as obtained by lu:
[L, U, P] = lu (A);
then a factorization of A+x*y.' can be obtained
either as
[L1, U1] = lu (L, U, P*x, y)
or
[L1, U1, P1] = lu (L, U, P, x, y)
The first form uses the unpivoted algorithm, which is faster, but less stable. The second form uses a slower pivoted algorithm, which is more stable.
The matrix case is done as a sequence of rank-1 updates; thus, for large enough k, it will be both faster and more accurate to recompute the factorization from scratch.
See also: lu, cholupdate, qrupdate.
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.
If given a second argument of '0', qr returns an economy-sized
QR factorization, omitting zero rows of R and the corresponding
columns of Q.
If the matrix A is full, 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).
If the matrix A is sparse, then compute the sparse
QR factorization of A, using CSPARSE. As the matrix Q
is in general a full matrix, this function returns the Q-less
factorization R of A, such that
R = chol (A' * A).
If the final argument is the scalar 0 and the number of rows is
larger than the number of columns, then an economy factorization is
returned. That is R will have only size (A,1) rows.
If an additional matrix B is supplied, then qr returns
C, where C = Q' * B. This allows the
least squares approximation of A \ B to be calculated
as
[C, R] = qr (A, B) x = R \ C
See also: chol, hess, lu, qz, schur, svd, qrupdate, qrinsert, qrdelete, qrshift.
Given a QR factorization of a real or complex matrix A = Q*R, Q unitary and R upper trapezoidal, return the QR factorization of A + u*v’, where u and v are column vectors (rank-1 update) or matrices with equal number of columns (rank-k update). Notice that the latter case is done as a sequence of rank-1 updates; thus, for k large enough, it will be both faster and more accurate to recompute the factorization from scratch.
The QR factorization supplied may be either full (Q is square) or economized (R is square).
Given a QR factorization of a real or complex matrix
A = Q*R, Q unitary and
R upper trapezoidal, return the QR factorization of
[A(:,1:j-1) x A(:,j:n)], where u is a column vector to be inserted
into A (if orient is "col"), or the
QR factorization of [A(1:j-1,:);x;A(:,j:n)], where x is a row
vector to be inserted into A (if orient is "row").
The default value of orient is "col". If orient is
"col", u may be a matrix and j an index vector
resulting in the QR factorization of a matrix B such that
B(:,j) gives u and B(:,j) = [] gives A.
Notice that the latter case is done as a sequence of k insertions;
thus, for k large enough, it will be both faster and more accurate to
recompute the factorization from scratch.
If orient is "col", the QR factorization supplied may
be either full (Q is square) or economized (R is square).
If orient is "row", full factorization is needed.
Given a QR factorization of a real or complex matrix
A = Q*R, Q unitary and
R upper trapezoidal, return the QR factorization of
[A(:,1:j-1) A(:,j+1:n)], i.e., A with one column deleted
(if orient is "col"), or the QR factorization of
[A(1:j-1,:);A(j+1:n,:)], i.e., A with one row deleted (if
orient is "row").
The default value of orient is "col".
If orient is "col", j may be an index vector
resulting in the QR factorization of a matrix B such that
A(:,j) = [] gives B. Notice that the latter case is done as
a sequence of k deletions; thus, for k large enough, it will be both faster
and more accurate to recompute the factorization from scratch.
If orient is "col", the QR factorization supplied may
be either full (Q is square) or economized (R is square).
If orient is "row", full factorization is needed.
Given a QR factorization of a real or complex matrix
A = Q*R, Q unitary and
R upper trapezoidal, return the QR factorization
of A(:,p), where p is the permutation
p = [1:i-1, shift(i:j, 1), j+1:n] if i < j
or
p = [1:j-1, shift(j:i,-1), i+1:n] if j < i.
QZ decomposition of the generalized eigenvalue problem (A x = s B x).
There are three ways to call this function:
lambda = qz (A, B)
Computes the generalized eigenvalues lambda of (A - s B).
[AA, BB, Q, Z, V, W, lambda] = qz (A, B)
Computes QZ decomposition, generalized eigenvectors, and generalized eigenvalues of (A - s B)
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
[AA,BB,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.
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-plane
"+"= non-negative real part: leading block has all eigenvalues in the closed right half-plane
Note: qz performs permutation balancing, but not scaling
(see XREFbalance). The order of output arguments was selected for
compatibility with MATLAB.
See also: eig, balance, lu, chol, hess, qr, qzhess, schur, svd.
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.
Compute the Schur decomposition of A.
The Schur decomposition is defined as
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 default for real matrices is a real Schur decomposition.
A complex decomposition may be forced by passing the flag
"complex".
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.
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).
Convert a real, upper quasi-triangular Schur form TR to a complex, upper triangular Schur form T.
Note that the following relations hold:
UR * TR * UR' = U * T * U' and
U' * U is the identity matrix I.
Note also that U and T are not unique.
See also: schur.
Reorders the real Schur factorization (U,S) obtained with the
schur function, so that selected eigenvalues appear in the upper left
diagonal blocks of the quasi triangular Schur matrix.
The logical vector select specifies the selected eigenvalues as they appear along S’s diagonal.
For example, given the matrix A = [1, 2; 3, 4], and its Schur
decomposition
[U, S] = schur (A)
which returns
U = -0.82456 -0.56577 0.56577 -0.82456 S = -0.37228 -1.00000 0.00000 5.37228
It is possible to reorder the decomposition so that the positive eigenvalue is in the upper left corner, by doing:
[U, S] = ordschur (U, S, [0,1])
See also: schur.
Determine the largest principal angle between two subspaces spanned by the columns of matrices A and B.
Compute the singular value decomposition of A
A = U*S*V'
The function svd normally returns only the vector of singular values.
When called with 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.
Query or set the underlying LAPACK driver used by svd.
Currently recognized values are "gesvd" and "gesdd".
The default is "gesvd".
When called from inside a function with the "local" option, the
variable is changed locally for the function and any subroutines it calls.
The original variable value is restored when exiting the function.
See also: svd.
Compute Householder reflection vector housv to reflect x to be the j-th column of identity, i.e.,
(I - beta*housv*housv')x = norm (x)*e(j) if x(j) < 0, (I - beta*housv*housv')x = -norm (x)*e(j) if x(j) >= 0
Inputs
vector
index into vector
threshold for zero (usually should be the number 0)
Outputs (see Golub and Van Loan):
If beta = 0, then no reflection need be applied (zer set to 0)
householder vector
Construct an orthogonal basis u of block Krylov subspace
[v a*v a^2*v … a^(k+1)*v]
using Householder reflections to guard against loss of orthogonality.
If V is a vector, then h contains the Hessenberg matrix
such that a*u == u*h+rk*ek', in which
rk = a*u(:,k)-u*h(:,k), and ek' is the vector
[0, 0, …, 1] of length k. Otherwise, h is
meaningless.
If V is a vector and k is greater than length (A) - 1,
then h contains the Hessenberg matrix such that a*u == u*h.
The value of nu is the dimension of the span of the Krylov subspace (based on eps1).
If b is a vector and k is greater than m-1, then h contains the Hessenberg decomposition of A.
The optional parameter eps1 is the threshold for zero. The default value is 1e-12.
If the optional parameter pflg is nonzero, row pivoting is used to improve numerical behavior. The default value is 0.
Reference: A. Hodel, P. Misra, Partial Pivoting in the Computation of Krylov Subspaces of Large Sparse Systems, Proceedings of the 42nd IEEE Conference on Decision and Control, December 2003.
Next: Functions of a Matrix, Previous: Basic Matrix Functions, Up: Linear Algebra [Contents][Index]