# fixed.qlessQRMatrixSolve

Solve system of linear equations (A'A)X = B for X using Q-less QR decomposition

Since R2020b

## Syntax

``X = fixed.qlessQRMatrixSolve(A,B)``
``X = fixed.qlessQRMatrixSolve(A,B,outputType)``
``X = fixed.qlessQRMatrixSolve(A,B,outputType,forgettingFactor)``
``X = fixed.qlessQRMatrixSolve(A,B,outputType,[],regularizationParameter)``
``X = fixed.qlessQRMatrixSolve(A,B,outputType,forgettingFactor,regularizationParameter)``

## Description

````X = fixed.qlessQRMatrixSolve(A,B)` solves the system of linear equations (A'A)X = B using QR decomposition, without computing the Q value.The result of this code is equivalent to computing[~,R] = qr(A,0); X = R\(R'\B)orX = (A'*A)\B```

example

````X = fixed.qlessQRMatrixSolve(A,B,outputType)` returns the solution to the system of linear equations (A'A)X = B as a variable with the output type specified by `outputType`.```

example

````X = fixed.qlessQRMatrixSolve(A,B,outputType,forgettingFactor)` returns the solution to the system of linear equations, with the `forgettingFactor` multiplied by R after each row of `A` is processed.```
````X = fixed.qlessQRMatrixSolve(A,B,outputType,[],regularizationParameter)` solves the matrix equation $\left({\lambda }^{2}{I}_{n}+A\text{'}A\right)X=B$ where λ is the `regularizationParameter`.```
````X = fixed.qlessQRMatrixSolve(A,B,outputType,forgettingFactor,regularizationParameter)` solves the matrix equation $A{\text{'}}_{\alpha ,\lambda }{A}_{\alpha ,\lambda }X=B$ where ${A}_{\alpha ,\lambda }=\left[\begin{array}{c}{\alpha }^{m}\lambda {I}_{n}\\ \left[\begin{array}{cccc}{\alpha }^{m}& & & \\ & {\alpha }^{m-1}& & \\ & & \ddots & \\ & & & \alpha \end{array}\right]\end{array}A\right],$ α is the `forgettingFactor`, λ is the `regularizationParameter`, and m is the number of rows in A.```

## Examples

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This example shows how to solve the system of linear equations $\left({\mathit{A}}^{\prime }\mathit{A}\right)\mathit{X}=\mathit{B}$ using QR decomposition, without explicitly calculating the Q factor of the QR decomposition.

```rng('default'); m = 6; n = 3; p = 1; A = randn(m,n); B = randn(n,p); X = fixed.qlessQRMatrixSolve(A,B)```
```X = 3×1 0.2991 0.0523 0.4182 ```

The `fixed.qlessQRMatrixSolve` function is equivalent to the following code, however the `fixed.qlessQRMatrixSolve` function is more efficient and supports fixed-point data types.

`X = (A'*A)\B`
```X = 3×1 0.2991 0.0523 0.4182 ```

This example shows how to specify an output data type to solve a system of equations with fixed-point data.

Define the data representing the system of equations. Define the matrix `A` as a zero-mean, normally distributed random matrix with a standard deviation of 1.

```rng('default'); m = 6; n = 3; p = 1; A0 = randn(m,n); B0 = randn(n,p);```

Specify fixed-point data types for `A` and `B` as to avoid overflow during the computation of QR.

```T.A = fi([],1,22,16); T.B = fi([],1,22,16); A = cast(A0,'like',T.A)```
```A = 0.5377 -0.4336 0.7254 1.8339 0.3426 -0.0630 -2.2589 3.5784 0.7147 0.8622 2.7694 -0.2050 0.3188 -1.3499 -0.1241 -1.3077 3.0349 1.4897 DataTypeMode: Fixed-point: binary point scaling Signedness: Signed WordLength: 22 FractionLength: 16 ```
`B = cast(B0,'like',T.B)`
```B = 1.4090 1.4172 0.6715 DataTypeMode: Fixed-point: binary point scaling Signedness: Signed WordLength: 22 FractionLength: 16 ```

Specify an output data type to avoid overflow in the back-substitution.

`T.X = fi([],1,29,12);`

Use the `fixed.qlessQRMatrixSolve` function to compute the solution, `X`.

`X = fixed.qlessQRMatrixSolve(A,B,T.X)`
```X = 0.2988 0.0522 0.4180 DataTypeMode: Fixed-point: binary point scaling Signedness: Signed WordLength: 29 FractionLength: 12 ```

Compare this result to the result of the built-in MATLAB® operations in double-precision floating-point.

`X0 = (A0'*A0)\B0`
```X0 = 3×1 0.2991 0.0523 0.4182 ```

## Input Arguments

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Coefficient matrix in the linear system of equations (A'A)X = B.

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

Input vector or matrix representing B in the linear system of equations (A'A)X = B.

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

Output data type, specified as a `numerictype` object or a numeric variable. If `outputType` is specified as a `numerictype` object, the output, `X`, will have the specified data type. If `outputType` is specified as a numeric variable, `X` will have the same data type as the numeric variable.

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

Forgetting factor, specified as a nonnegative scalar between 0 and 1. The forgetting factor determines how much weight past data is given. The `forgettingFactor` value is multiplied by the output of the QR decomposition, R after each row of `A` is processed.

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

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

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Solution, returned as a vector or matrix. If `A` is an m-by-n matrix and `B` is an m-by-p matrix, then `X` is an `n`-by-`p` matrix.

## Version History

Introduced in R2020b

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