qz

Generalized Schur (QZ) factorization for generalized eigenvalues

Syntax

[AA,BB,Q,Z] = qz(A,B)

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

[___] = qz(A,B,mode)

Description

[AA,BB,Q,Z] = qz(A,B) performs a QZ factorization on square matrices A and B such that Q*A*Z = AA and Q*B*Z = BB.

If A and B are real, then AA and BB are upper quasitriangular matrices.
If A and B are complex, then AA and BB are triangular.
Q and Z are unitary matrices.

example

[AA,BB,Q,Z,V,W] = qz(A,B) also returns matrices V and W whose columns are the right and left generalized eigenvectors, respectively, for A and B.

example

[___] = qz(A,B,mode) returns a real decomposition if mode is "real" or returns a possibly complex decomposition if mode is "complex". Use this syntax with any of the output argument combinations in the previous syntaxes.

example

Examples

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QZ Factorization

Open Live Script

Calculate the QZ factorization of two 3-by-3 matrices.

A = [1 7 3; 2 9 12; 5 22 7];
B = [3 1 0; 0 3 1; 0 0 3];
[AA,BB,Q,Z] = qz(A,B)

AA = 3×3

   23.5574    1.4134  -14.3485
         0   -0.5776    2.7629
         0         0   -8.6720

BB = 3×3

    3.5845   -0.1090   -0.6024
         0    2.7599    0.8430
         0         0    2.7292

Q = 3×3

    0.2566    0.6353    0.7284
   -0.9477    0.3134    0.0604
   -0.1899   -0.7058    0.6824

Z = 3×3

    0.1502   -0.9664   -0.2088
    0.4689    0.2556   -0.8455
    0.8704    0.0291    0.4915

Verify that the norms of AA - Q*A*Z, BB - Q*B*Z, Q'*Q - eye(size(Q)), and Z'*Z - eye(size(Z)) are 0, within machine precision.

norm(AA - Q*A*Z)

ans = 
6.3184e-15

norm(BB - Q*B*Z)

ans = 
1.7936e-15

norm(Q'*Q - eye(size(Q)))

ans = 
8.8320e-16

norm(Z'*Z - eye(size(Z)))

ans = 
5.1071e-16

Generalized Eigenvectors

Open Live Script

Calculate the QZ factorization and also return the generalized eigenvectors of two 2-by-2 matrices.

A = [10 -7; -3 2];
B = [7 3; 12 9];
[AA,BB,Q,Z,V,W] = qz(A,B)

AA = 2×2

   11.9600   -4.3532
         0   -0.0836

BB = 2×2

    1.6381   -2.9374
         0   16.4830

Q = 2×2

   -0.9597    0.2811
    0.2811    0.9597

Z = 2×2

   -0.5752    0.8180
    0.8180    0.5752

V = 2×2

   -0.7031    0.6960
    1.0000    1.0000

W = 2×2

   -1.0000    0.2929
    0.4537    1.0000

Verify that the elements of Q*A*Z - AA and Q*B*Z - BB are 0, within machine precision.

Q*A*Z - AA

ans = 2×2
10^-14 ×

         0    0.1776
   -0.1034   -0.1360

Q*B*Z - BB

ans = 2×2
10^-14 ×

   -0.0222         0
    0.0888   -0.3553

Calculate the generalized eigenvalues and right and left eigenvectors of A and B by using the eig function. Verify that the elements of A*V - B*V*D and W'*A - D*W'*B are 0, within machine precision.

[V,D,W] = eig(A,B);
A*V - B*V*D

ans = 2×2
10^-14 ×

         0   -0.2297
   -0.7105   -0.0860

W'*A - D*W'*B

ans = 2×2
10^-14 ×

    0.7105   -0.3553
   -0.1235   -0.0625

Complex QZ Factorization

Open Live Script

Calculate the complex QZ factorization of two 3-by-3 matrices.

A = [1/sqrt(2) 1 0; 0 1 1; 0 1/sqrt(2) 1];
B = [0 1 1; -1/sqrt(2) 0 1; 1 -1/sqrt(2) 0];
[AAc,BBc,Qc,Zc] = qz(A,B)

AAc = 3×3 complex

   0.5011 - 0.8679i   0.0332 - 1.0852i   0.3687 + 0.9278i
   0.0000 + 0.0000i   0.1848 - 0.0000i  -0.6334 - 0.3673i
   0.0000 + 0.0000i   0.0000 + 0.0000i   0.5590 + 0.9682i

BBc = 3×3 complex

   1.0022 + 0.0000i   0.3136 + 0.0711i  -0.0280 + 0.5966i
   0.0000 + 0.0000i   1.3388 + 0.0000i   0.1572 + 0.6846i
   0.0000 + 0.0000i   0.0000 + 0.0000i   1.1180 + 0.0000i

Qc = 3×3 complex

   0.5379 + 0.2210i   0.4604 - 0.3553i   0.3214 - 0.4693i
   0.2172 + 0.3386i   0.4018 - 0.0188i  -0.7698 + 0.2895i
  -0.3719 - 0.6014i   0.7068 - 0.0213i  -0.0000 - 0.0000i

Zc = 3×3 complex

   0.2514 + 0.0413i  -0.7279 - 0.4531i  -0.4470 - 0.0135i
  -0.1000 - 0.6068i   0.3328 - 0.3332i  -0.3326 + 0.5379i
   0.6391 + 0.3853i   0.1423 - 0.1511i   0.2996 + 0.5570i

Calculate the real QZ decomposition of A and B by specifying mode as "real". The generalized Schur form of A is quasitriangular, indicating that it has complex eigenvalues.

[AAr,BBr,Qr,Zr] = qz(A,B,"real")

AAr = 3×3

    0.1464    1.1759    0.3094
         0    1.0360    1.2594
         0   -0.8587    0.3212

BBr = 3×3

    1.0607   -0.5952    0.1441
         0    1.6676         0
         0         0    0.8481

Qr = 3×3

    0.0000   -0.0000   -1.0000
   -0.7882   -0.6154    0.0000
   -0.6154    0.7882   -0.0000

Zr = 3×3

   -0.7071    0.2610   -0.6572
    0.5000   -0.4727   -0.7257
   -0.5000   -0.8417    0.2037

For triangular AAc, compute the eigenvalues by using diag(AA)./diag(BB).

diag(AAc)./diag(BBc)

ans = 3×1 complex

   0.5000 - 0.8660i
   0.1381 - 0.0000i
   0.5000 + 0.8660i

For quasitriangular AAr, compute the eigenvalues by using the ordeig function.

ordeig(AAr,BBr)

ans = 3×1 complex

   0.1381 + 0.0000i
   0.5000 + 0.8660i
   0.5000 - 0.8660i

Input Arguments

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`A`, `B` — Input matrices
square matrices

Input matrices, specified as real or complex square matrices. The dimensions of A and B must be the same.

Data Types: single | double
Complex Number Support: Yes

`mode` — Decomposition mode
`"complex"` (default) | `"real"`

Decomposition mode, specified as one of these values:

"complex" — qz returns a possibly complex decomposition, and AA and BB are triangular.
"real" — qz returns a real decomposition, and AA and BB are quasitriangular.

Output Arguments

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`AA`, `BB` — Generalized Schur forms of `A` and `B`
square matrices

Generalized Schur forms of A and B, returned as upper triangular or quasitriangular square matrices.

When the decomposition is complex and AA is triangular, then the diagonal elements a = diag(AA) and b = diag(BB) are the generalized eigenvalues that satisfy A*V*b = B*V*a and b'*W'*A = a'*W'*B.
When the decomposition is real and AA is quasitriangular, you must further reduce the 2-by-2 blocks to obtain the eigenvalues of the full system. Each 2-by-2 block in AA corresponds to a 2-by-2 diagonal block at the same location in BB.

`Q`, `Z` — Unitary factors
square matrices

Unitary factors, returned as square matrices that satisfy Q*A*Z = AA and Q*B*Z = BB.

`V` — Right eigenvectors
square matrix

Right eigenvectors, returned as a square matrix whose columns are the generalized right eigenvectors of the pair (A,B). The eigenvectors satisfy A*V = B*V*D, where D contains the generalized eigenvalues of the pair along its main diagonal. Use the eig function to return D and the ordeig function to return the diagonal elements of D.

Different machines and releases of MATLAB^® can produce different eigenvectors that are still numerically accurate:

For real eigenvectors, the sign of the eigenvectors can change.
For complex eigenvectors, the eigenvectors can be multiplied by any complex number of magnitude 1.
For a multiple eigenvalue, its eigenvectors can be recombined through linear combinations. For example, if Ax = λx and Ay = λy, then A(x+y) = λ(x+y), so x+y also is an eigenvector of A.

`W` — Left eigenvectors
square matrix

Left eigenvectors, returned as a square matrix whose columns are the generalized left eigenvectors of the pair (A,B). The eigenvectors satisfy W'*A = D*W'*B, where D contains the generalized eigenvalues of the pair along its main diagonal. Use the eig function to return D and the ordeig function to return the diagonal elements of D.

Different machines and releases of MATLAB can produce different eigenvectors that are still numerically accurate:

For real eigenvectors, the sign of the eigenvectors can change.
For complex eigenvectors, the eigenvectors can be multiplied by any complex number of magnitude 1.
For a multiple eigenvalue, its eigenvectors can be recombined through linear combinations. For example, if Ax = λx and Ay = λy, then A(x+y) = λ(x+y), so x+y also is an eigenvector of A.

More About

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Quasitriangular Matrix

An upper quasitriangular matrix can result from the Schur decomposition or generalized Schur (QZ) decomposition of a real matrix. An upper quasitriangular matrix is block upper triangular, with 1-by-1 and 2-by-2 blocks of nonzero values along the diagonal.

The eigenvalues of these diagonal blocks are also the eigenvalues of the matrix. The 1-by-1 blocks correspond to real eigenvalues, and the 2-by-2 blocks correspond to complex conjugate eigenvalue pairs.

Unitary Matrix

An invertible complex square matrix U is unitary if its conjugate transpose is also its inverse, that is, if $U^{*} U = U U^{*} = I$ .

Tips

You can calculate the generalized eigenvalues that solve the generalized eigenvalue problem $A x = λ B x$ from the QZ factorization. For triangular AA, calculate the eigenvalues using diag(AA)./diag(BB). For quasitriangular AA, calculate the eigenvalues using ordeig(AA,BB).

Extended Capabilities

Thread-Based Environment
Run code in the background using MATLAB® `backgroundPool` or accelerate code with Parallel Computing Toolbox™ `ThreadPool`.

This function fully supports thread-based environments. For more information, see Run MATLAB Functions in Thread-Based Environment.

Version History

Introduced before R2006a

qz

Syntax

Description

Examples

QZ Factorization

Generalized Eigenvectors

Complex QZ Factorization

Input Arguments

A, B — Input matrices square matrices

mode — Decomposition mode "complex" (default) | "real"

Output Arguments

AA, BB — Generalized Schur forms of A and B square matrices

Q, Z — Unitary factors square matrices

V — Right eigenvectors square matrix

W — Left eigenvectors square matrix

More About

Quasitriangular Matrix

Unitary Matrix

Tips

Extended Capabilities

Thread-Based Environment Run code in the background using MATLAB® backgroundPool or accelerate code with Parallel Computing Toolbox™ ThreadPool.

Version History

See Also

`A`, `B` — Input matrices
square matrices

`mode` — Decomposition mode
`"complex"` (default) | `"real"`

`AA`, `BB` — Generalized Schur forms of `A` and `B`
square matrices

`Q`, `Z` — Unitary factors
square matrices

`V` — Right eigenvectors
square matrix

`W` — Left eigenvectors
square matrix

Thread-Based Environment
Run code in the background using MATLAB® `backgroundPool` or accelerate code with Parallel Computing Toolbox™ `ThreadPool`.