fixed.qrAB

Compute C = Q'B and upper-triangular factor R

Since R2020b

Syntax

[C,R] = fixed.qrAB(A,B)

[C,R] = fixed.qrAB(A,B,regularizationParameter)

Description

[C,R] = fixed.qrAB(A,B) computes C = Q'B and upper-triangular factor R. The function simultaneously performs Givens rotations to A and B to transform A into R and B into C.

This syntax is equivalent to

[C,R] = qr(A,B)

[C,R] = fixed.qrAB(A,B,regularizationParameter) computes C and R using a regularization parameter value specified by regularizationParameter. When a regularization parameter is specified, the function simultaneously performs Givens rotations to transform

$$\left[\begin{array}{l}\lambda I_n\\ A\end{array}\right]\to R$$

and

$$\left[\begin{array}{l}{0}_{n,p}\\ B\end{array}\right]\to C$$

where A is an m-by-n matrix, B is a m-by-p matrix, and λ is the regularization parameter.

This syntax is equivalent to

[Q,R] = qr([regularizationParameter*eye(n); A], 0);
C = Q'[zeros(n,p);B];

Examples

Compute C and R Factors

This example shows how to compute the upper-triangular factor $\mathit{R}$, and $\mathit{C}={\mathit{Q}}^{\prime }\mathit{b}$.

Define the input matrices, A, and b.

rng('default');
m = 6;
n = 3;
p = 1;
A = randn(m,n)

A = 6×3

    0.5377   -0.4336    0.7254
    1.8339    0.3426   -0.0631
   -2.2588    3.5784    0.7147
    0.8622    2.7694   -0.2050
    0.3188   -1.3499   -0.1241
   -1.3077    3.0349    1.4897

b = randn(m,p)

b = 6×1

    1.4090
    1.4172
    0.6715
   -1.2075
    0.7172
    1.6302

The fixed.qrAB function returns the upper-triangular factor, $\mathit{R}$, and $\mathit{C}={\mathit{Q}}^{\prime }\mathit{b}$.

[C, R] = fixed.qrAB(A,b)

C = 3×1

   -0.3284
    0.4055
    2.5481

R = 3×3

    3.3630   -2.8841   -1.0421
         0    4.8472    0.6885
         0         0    1.3258

Solve System of Linear Equations Using Regularization

This example shows how to solve a system of linear equations, $\mathit{Ax}=\mathit{b}$, by computing the upper-triangular factor $\mathit{R}$, and $\mathit{C}={\mathit{Q}}^{\prime }\mathit{b}$. A regularization parameter can improve the conditioning of least squares problems, and reduce the variance of the estimates when solving linear systems of equations.

Define input matrices, A, and b.

rng('default');
m = 50;
n = 5;
p = 1;
A = randn(m,n);
b = randn(m,p);

Use the fixed.qrAB function to compute the upper-triangular factor, $\mathit{R}$, and $\mathit{C}={\mathit{Q}}^{\prime }\mathit{b}$.

[C, R] = fixed.qrAB(A, b, 0.01)

C = 5×1

   -0.6361
    1.7663
    1.5892
   -2.0638
   -0.1327

R = 5×5

    9.0631    0.7471    0.4126   -0.3606    0.1883
         0    7.2515   -1.1145    0.6011   -0.7544
         0         0    7.6132   -0.9460   -0.7062
         0         0         0    6.3065   -2.3238
         0         0         0         0    5.9297

Use this result to solve $\mathit{Ax}=\mathit{b}$ using x = R\C. Compute x = R\C using the fixed.qrMatrixSolve function.

x = fixed.qrMatrixSolve(R,C)

x = 5×1

   -0.1148
    0.2944
    0.1650
   -0.3355
   -0.0224

Compare the result to computing x = A\b directly.

x = A\b

x = 5×1

   -0.1148
    0.2944
    0.1650
   -0.3355
   -0.0224

Input Arguments

A — Input coefficient matrix

matrix

Input coefficient matrix, specified as a matrix.

Data Types: single | double | fi
Complex Number Support: Yes

B — Right-hand side matrix

matrix

Right-hand side matrix, specified as a matrix.

Data Types: single | double | fi
Complex Number Support: Yes

regularizationParameter — Regularization parameter

0 (default) | nonnegative scalar

Regularization parameter, specified as a nonnegative scalar. Small, positive values of the regularization parameter can improve the conditioning of the problem and reduce the variance of the estimates. While biased, the reduced variance of the estimate often results in a smaller mean squared error when compared to least-squares estimates.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | fi

Output Arguments

C — Linear system factor

matrix

Linear system factor, returned as a matrix that satisfies C = Q'B.

R — Upper-triangular factor

matrix

Upper-triangular factor, returned as a matrix that satisfies A = QR.

Extended Capabilities

C/C++ Code Generation

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

Generate code for double-precision, single-precision, and fixed-point data types.

Fixed-Point Conversion

Design and simulate fixed-point systems using Fixed-Point Designer™.

A and B must be signed and use binary-point scaling. Slope-bias representation is not supported for fixed-point data types.

Version History

Introduced in R2020b