QR Factorization

Factor arbitrary matrix into unitary and upper triangular components

Library

Math Functions / Matrices and Linear Algebra / Matrix Factorizations

dspfactors

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Description

The QR Factorization block uses a sequence of Householder transformations to triangularize the input matrix A. The block factors a column permutation of the M-by-N input matrix A as

A_e = QR

The column-pivoted matrix A_e contains the columns of A permuted as indicated by the contents of length-N permutation vector E.

Ae = A(:,E)					% Equivalent MATLAB code

The block selects a column permutation vector E, which ensures that the diagonal elements of matrix R are arranged in order of decreasing magnitude.

$$\left|r_{i+1,j+1}\right|<\left|r_{i,j}\right|i=j$$

The size of matrices Q and R depends on the setting of the Output size parameter:

• When you select Economy for the output size, Q is an M-by-min(M,N) unitary matrix, and R is a min(M,N)-by-N upper-triangular matrix.

  [Q R E] = qr(A,0)   % Equivalent MATLAB code

• When you select Full for the output size, Q is an M-by-M unitary matrix, and R is a M-by-N upper-triangular matrix.

  [Q R E] = qr(A)      % Equivalent MATLAB code

The block treats length-M unoriented vector input as an M-by-1 matrix.

QR factorization is an important tool for solving linear systems of equations because of good error propagation properties and the invertibility of unitary matrices:

Q ^–1 = Q'

where Q' is the complex conjugate transpose of Q.

Unlike LU and Cholesky factorizations, the matrix A does not need to be square for QR factorization. However, QR factorization requires twice as many operations as LU Factorization (Gaussian elimination).

Parameters

Output size

Specify the size of output matrices Q and R:

• Economy — When this output size is selected, the block outputs an M-by-min(M,N) unitary matrix Q and a min(M,N)-by-N upper-triangular matrix R.

• Full — When this output size is selected, the block outputs an M-by-M unitary matrix Q and a M-by-N upper-triangular matrix R.

Simulate using

• Interpreted execution (default)

  Simulate model using the MATLAB^® interpreter. This option shortens startup time and has faster simulation speed compared to Code generation.

• Code generation

  Simulate model using generated C code. The first time you run a simulation, Simulink^® generates C code for the block. The C code is reused for subsequent simulations, as long as the model does not change. This option requires additional startup time but provides faster subsequent simulations.

References

Golub, G. H., and C. F. Van Loan. Matrix Computations. 3rd ed. Baltimore, MD: Johns Hopkins University Press, 1996.

Supported Data Types

Port	Supported Data Types
Input	Double-precision floating point Single-precision floating point
Output	Double-precision floating point Single-precision floating point

Extended Capabilities

C/C++ Code Generation

Generate C and C++ code using Simulink® Coder™.

Generated code relies on the memcpy or memset function (string.h) under certain conditions.

Version History

Introduced before R2006a