---
title: "QR factorization without pivoting"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{QR factorization without pivoting}
  %\VignetteEngine{knitr::rmarkdown}
  \usepackage[utf8]{inputenc}
---

# Introduction

This package implements in R the LAPACK routine DGEQRF to obtain the QR factorization without pivoting of a real matrix.

This package aims to solve one of the issues of the qr() function of base R, it returns the solution obtained using QR factorization with pivoting. Therefore, the product Q\*R is not equal to the factorized matrix.

# Further information

For further explanation on the DGEQRF routine, read the following [document](https://www.netlib.org/lapack/explore-html/dd/d9a/group__double_g_ecomputational_ga3766ea903391b5cf9008132f7440ec7b.html).

# Example on how to use QR

```{r echo=FALSE,include=FALSE}
library(QR)
set.seed(26112011)
```

First, we need to define the matrix we want to factorize. This matrix can be of any m x n dimensions and the entries must be real numbers.

```{r echo=TRUE, include=TRUE}
# Let's sample a random square-matrix
A<-matrix(runif(121,min = -100, max = 100), 11, 11)
```

Once, we have the matrix, it is time to use the QR function:

```{r echo=TRUE, include=TRUE, collapse=TRUE}
QRres<-QR(A)
QRres
```

We can observe that the output is a list with 4 elements:

-   qr: This element is a matrix returned by the DGEQRF routine with the same dimensions as A. The upper triangle contains the **R** of the decomposition and the lower triangle contains information on the **Q** of the decomposition (stored in compact form).

-   qraux: This element is a vector returned by the DGEQRF routine of length ncol(A) which contains additional information on **Q**.

-   Q: This element is an orthogonal matrix such that Q\*R is equal to A.

-   R: This element is an upper triangular matrix such that Q\*R is the input matrix.


Let's print the Q and R matrices obtained using QR().

```{r echo=TRUE, include=TRUE, collapse=TRUE}
QRres$Q

QRres$R
```

We can check if Q is orthogonal by multiplying Q and $Q^T$.

```{r echo=TRUE, include=TRUE, collapse=TRUE}
all.equal(QRres$Q%*%t(QRres$Q),diag(11))
```

Also, we can easily check that R is upper triangular.

```{r echo=TRUE, include=TRUE, collapse=TRUE}
all.equal(QRres$R[lower.tri(QRres$R)],rep(0,11*10/2))
```

Finally, we can test if the factorization is correct by multiplying Q and R to obtain A.

```{r echo=TRUE, include=TRUE, collapse=TRUE}
all.equal(QRres$Q%*%QRres$R,A)
```

# Comparison between QR() and qr() from base R.

In the help file of the functions to reconstruct the Q and R matrices from base R we find the following example:

```{r echo=TRUE, include=TRUE, collapse=TRUE}
# example of pivoting
x <- cbind(int = 1,
           b1 = rep(1:0, each = 3), b2 = rep(0:1, each = 3),
           c1 = rep(c(1,0,0), 2), c2 = rep(c(0,1,0), 2), c3 = rep(c(0,0,1),2))
x # is singular, columns "b2" and "c3" are "extra"
a <- qr(x)
```

If we multiply the obtained Q and R, we do not obtain x:

```{r echo=TRUE, include=TRUE, collapse=TRUE}
all.equal(qr.Q(a)%*%qr.R(a),x)
```

This is caused because the solution was obtained using column pivoting.

If we use the function QR(), the product of Q and R is equal to x:

```{r echo=TRUE, include=TRUE, collapse=TRUE}
all.equal(QR(x)$Q%*%QR(x)$R,x)
```

**Note**: The unpivoted solution is NOT equal to the pivoted solution after a transformation that undoes the column pivoting.
