# Householder decomposition

(ns morelinear.householder
(:require [clojure.core.matrix :refer :all]))


The Householder decomposition is an alternate method to the Gram-Schmidt decomposition for build a QR matrix pair. instead of building an orthonormal basis it takes a more direct approach similar to the Gaussian LU procedure by just zeroing out columns to get the desired shape. The difference from the LU is that now instead of using elementary matrices to do row operations to get zeroes we will restrict ourselves to using elementary reflectors.

## elementary reflector

An elementary reflector does what's written on the label, it's a matrix/linear-system which when given a vector produces its reflection across a N-1 dimensional hyperplane (in 2D it's a line and in 3D it's a plane). In the next section we will deal with selecting the correct hyperplane, but for the time being we will just focus on building such a matrix.

Their key property is that they're both orthonormal and symmetric.

Orthonormal
their rows (and columns) form an orthogonal basis and each row (and column) is of unit length. As a consequence, given an orthonormal matrix Q its inverse will be its transpose. In QQT the off-diagonal terms will be inner products of different basis vectors. Since they're orthogonal these inner product will be zero. The diagonal elements will be inner products of vectors with themselves so it will equal to their length. Since the rows are all unit length the diagonal will be all ones.
Symmetric
means that it's equal to its own transpose. As a consequence a symmetric + orthonormal matrix will be it's own inverse. ie. QQ=I. This makes some sense b/c reflecting something twice gives you the same things back.
A product of orthonormal matrices will be orthonormal
b/c given two orthonormal matrices U and V we can test for orthonormality: (UV)(UV)T=UVVTUT=I

So if we carry out a series of reflections Qk..Q2..Q1A we can combine them into one matrix Q which will be guaranteed to be orthonormal as well. It however will not necessarily be a reflection matrix!

To build a reflector matrix we need to find a nice concise mechanism to define the hyperplane over which it reflects. If our space is RN then the hyperplane will be N-1 dimensions and at first blush we seem to need N-1 vectors to define it. For instance in 3D space any two vectors not on the same line will define a 2D plane (ie. a*v1 + b*v2 for all a and b). But this method doesn't really scale b/c as N increases so does the number of vectors you need. The shortcut is that actually all planes have vectors orthogonal to the hyper plane. These vectors all lie on the same line and we can just choose one, call it u, and let it represent that remaining Nth dimension. Now you can simply say that the hyperplane is all the vectors orthogonal to u. Or more formally, all vectors not-in-the-span of u are the hyperplane

Now that we have a way to define a plane we need to work through the mechanics of relfecting an arbitrary vector x across the hyperplane. x can be broken up into two separate vectors: One that lies in the plane and one that is orthogonal to the plane. The component that lies in the plane is unaffected by the reflection while the component that is orthogonal is in the direction of u and is flipped by just getting its negative. To do this procedure mathematically we start with a vector x and subtract twice its component in the direction of the u:

• utx*/||u|| is the amount of x in the direction of u (a scalar)
• uutx/||u||2 is the component x in the direction of u (a vector)
• Here we notice that we can subsitute the inner product utu for ||u||2
• uutx/utu
• x - 2uutx/utu is you subtracting that vector component twice to get its reflection
• (I-2uut/utu)x is how we'd factor the x back out

Notice how in the last step we managed to factor out the x , so we can subsitute it with any other vector to get a reflection. The matrix (I-2uut/utu) to its left is the reflector matrix. It's defined uniquely by u and is independent of x.

Note: We will see in the next section that thought it would make life easier, we can't safely assume u is unit length

(defn elementary-reflector
"Build a matrix that will reflect vector across the hyperplane orthogonal to REFLECTION-AXIS"
[reflection-axis]
(let [dimension (dimension-count reflection-axis 0)]
(sub (identity-matrix dimension)
(mul (outer-product reflection-axis reflection-axis)
(/ 2 (length-squared reflection-axis))))))


For example:

(pm (elementary-reflector [43.0 36.0 38.0 90.0]))
;; [[ 0.709 -0.244 -0.258 -0.610]
;;  [-0.244  0.796 -0.216 -0.511]
;;  [-0.258 -0.216  0.772 -0.539]
;;  [-0.610 -0.511 -0.539 -0.277]]
;; nil

(pm (mmul (elementary-reflector [43.0 36.0 38.0 90.0])
[43.0 36.0 38.0 90.0]))
;; [-43.000 -36.000 -38.000 -90.000]
;; nil


## elementary coordinate reflector

Circling back to our original intent, we were trying to use reflectors to clear rows and build an upper triangular matrix (the R in the QR)

The first thing we want to do is have a way to zero out the first column of a matrix, ie A - ,1. We'd like to build a special elementary reflector Q1 that reflected that first column on to the elementary vector e1 (that's [ 1 0 0 0.. 0 ] ). If we had this matrix then Q1A would leave everything under the the first column zeroed out.

Generalizing the problem a bi, this is a bit of an inversion of what we did in the previous section. Instead of taking a hyperplane and reflecting over it, we now know what we want to reflect and where we want to reflect it to - we just need to find the right plane to do it. This plane lies between where we start and where we want to reflect to. If you picture it in 2D space then you could take the two vectors add up their norms and you will get a vector that bisects them (forming a equilateral diamond shape with the point lieing on the bisecting line). In higher dimensions it will get a bit more complicated as you need more and more vectors.

Fortunately we know we can define the plane with the orthogonal vector. To get that we just subtract the two vector norms and you will find that you get a vector orthogonal to that bisection vector/plane.

\begin{equation} u = x - ||x||e_{1} \end{equation}

Strain your brain and try to picture it in 2D and in 3D and it should make sense.

Once you an orthogonal vector to the bisecting plane, you just feed it into our previous function and get the reflection matrix

(defn elementary-coordinate-reflector
"Build a matrix that will reflect the INPUT-VECTOR on to the COORDINATE-AXIS"
[input-vector coordinate-axis]
(let [vector-orthogonal-to-reflection-plane
(sub input-vector
(mul coordinate-axis
(length input-vector)))]
(if (zero-matrix? vector-orthogonal-to-reflection-plane)
;; degenerate case where the input is on the coordinate axis
(identity-matrix (dimension-count input-vector 0))
;; normal case
(elementary-reflector vector-orthogonal-to-reflection-plane))))



For instance we can take some random vector and say we want to reflect it onto the e1

(pm (elementary-coordinate-reflector [24 77 89 12]
[1 0 0 0]))
;; [[0.199  0.638  0.737  0.099]
;;  [0.638  0.492 -0.587 -0.079]
;;  [0.737 -0.587  0.321 -0.091]
;;  [0.099 -0.079 -0.091  0.988]]
;; nil


We got some seemingly random matrix out. If we then multiply it times our random vector, it reflects perfectly to e1

(pm (mmul (elementary-coordinate-reflector [24 77 89 12]
[1 0 0 0])
[24 77 89 12])))
;; [120.706 -0.000 -0.000 -0.000]
;; nil


## Zeroing the first column

Now putting all the pieces together, given some matrix A we can get back a reflector to zero out its first column

(defn first-column-reflector
"Build a matrix that will reflect the first column of INPUT-MATRIX
on to the first elementary vector [ 1 0 0 .. 0 ]"
[input-matrix]
(elementary-coordinate-reflector (get-column input-matrix
0)
(get-row (identity-matrix (dimension-count input-matrix 0)) 0)))


This is really just a wrapper for the previous function. Now we can test it by writing out a random matrix and zeroing out its first column

(pm (first-column-reflector [[43.0 36.0 38.0 90.0]
[21.0 98.0 55.0 48.0]
[72.0 13.0 98.0 12.0]
[28.0 38.0 73.0 20.0]]))
;; [[0.473  0.231  0.792  0.308]
;;  [0.231  0.899 -0.348 -0.135]
;;  [0.792 -0.348 -0.192 -0.463]
;;  [0.308 -0.135 -0.463  0.820]]
;; nil

(let [A [[43.0 36.0 38.0 90.0]
[21.0 98.0 55.0 48.0]
[72.0 13.0 98.0 12.0]
[28.0 38.0 73.0 20.0]]]
(pm (mmul (first-column-reflector A)
A)))
;; [[90.874  61.690 130.830 69.349]
;;  [ 0.000  86.731  14.280 57.059]
;;  [-0.000 -25.637 -41.613 43.058]
;;  [ 0.000  22.975  18.706 32.078]]
;; nil


## Zeroing out the second column and so on..

Now we hit a bit of a problem. You can use the same method to make some matrix Q2 that will zero out the second column, but when you combine the two and try doing Q2Q1A you will see that Q2 is messing up the first column - so we lose the progress we'd made in the first step. We may have gotten the first column to lie on the coordinate vector after Q1A, but when you reflect it again it moves away from the coordinate vector b/c all columns are reflected at each step.

In the LU Gaussian Elimination method we didn't have this problem b/c clearing subsequent columns was guaranteed to leave you previous columns intact (b/c shuffling rows would just be moving around zeroes from the pervious columns). Now this guarantee is gone so we need to find a way to reflect some matrix columns and not others

The solution is thinking in terms of block matrices. When we say we need to clear the second column we can spell that out as : we want to take the result of our first reflector Q1A and now clearing everything under the (2,2) position. To avoid touching the first column we construct Q2 with the following form:

\begin{equation} Q_{2} \\= \begin{bmatrix} 1 & 0\\ 0 & S_{ n-1, m-1 }\\ \end{bmatrix} \end{equation}

Notice how when we multiply this matrix times Q1A the first column is left untouched

\begin{equation} Q_2(Q_1A) \\= \begin{bmatrix} 1 & 0\\ 0 & S\\ \end{bmatrix} \begin{bmatrix} (Q_{1}A)_{1,1} & (Q_{1}A)_{1,*}\\ 0 & (Q_{1}A)_{n-1,m-1}\\ \end{bmatrix} \\= \begin{bmatrix} (Q_{1}A)_{1,1} & (Q_{1}A)_{1,*}\\ 0 & S(Q_{1}A)_{n-1,m-1}\\ \end{bmatrix} \end{equation}

Now also notice that the n-1 by m-1 submatrix S will multiple times a submatrix of Q1A which has that (2,2) position now in the (1,1) position.

We've also got a bit of bonus b/c in the resulting matrix the only "new" entry we need to worry about is S(Q1A)n-1,m-1 - the first column and row have remained the same. In this submatrix product we need to again clear the first column because it's the second column of our overall matrix. Choosing an appropriate S matrix to do it mirrors the process we used to clear the first column of A - the only difference being that the dimension is one smaller.

When tackling the third column we do this again, getting the next submatrix of S(Q1A)n-1,m-1. At each step we are reducing the first column, grabbing the result's submatrix and calling the procedure again - until we are out of things to reduce

(defn reduce-to-r
"Reduce a matrix to a lower triangular orthonormal matrix"
[input-matrix]
(do (assign! input-matrix
(mmul (first-column-reflector input-matrix)
input-matrix))
(if (or (= 1 (row-count input-matrix))
(= 1 (column-count input-matrix)))
input-matrix ;; base case
(recur (submatrix input-matrix
1
(dec (row-count input-matrix))
1
(dec (column-count input-matrix)))))))


Note:

• This submatrix function is interesting b/c it will not make a copy of the matrix. Instead it will return a matrix object that shares its underlying data/memory with the parent matrix. So as we reduce the submatrices, the orginal matrix is being reduced as well
• The mmul matrix multiplication will unfortunately produce a temporary intermediary matrix which will then get copied into the matrix/submatrix. Other more advanced matrix libraries may have ways to do this in-place.

Now to test it I'm reusing the same random matrix from the previous example:

(let [A (mutable [[43.0 36.0 38.0 90.0]
[21.0 98.0 55.0 48.0]
[72.0 13.0 98.0 12.0]
[28.0 38.0 73.0 20.0]])]

(pm A)
;; [[43.000 36.000 38.000 90.000]
;;  [21.000 98.000 55.000 48.000]
;;  [72.000 13.000 98.000 12.000]
;;  [28.000 38.000 73.000 20.000]]
;; nil
(reduce-to-r A)
(pm A))
;; [[90.874 61.690 130.830  69.349]
;;  [ 0.000 93.313  29.311  49.102]
;;  [-0.000  0.000  37.767 -48.089]
;;  [ 0.000 -0.000   0.000 -37.619]]
;; nil


If you looking at the result I got when running (first-column-reflector ..) then you'll see that the first column and row have been preserved as we expect.

It's also important to note that everything we'd discussed also works on overdefined/skinny matrices. ie. where you have more equations than unknowns

(let [A (mutable [[43.0 36.0 38.0 ]
[21.0 98.0 55.0 ]
[72.0 13.0 98.0 ]
[28.0 38.0 73.0 ]])]

(pm A)
;;[[43.000 36.000 38.000]
;; [21.000 98.000 55.000]
;; [72.000 13.000 98.000]
;; [28.000 38.000 73.000]]
;; nil
(reduce-to-r A)
(pm A))
;; [[90.874 61.690 130.830]
;;  [ 0.000 93.313  29.311]
;;  [-0.000  0.000  37.767]
;;  [ 0.000 -0.000   0.000]]


## Getting the reflectors

The last section managed to get the R in the QR. The next step is combining all these intermediary relfectors (those S matrices) into a matrix Q

In Matrix Computation by Golub it's actually recommended to keep a list of the reflections - if necessary.

The recommended way of combining them is by working backwards from the last iteration step where the reflector matrix is just 1. Going back up one iteration we would take 1 and combine it with the first-column-reflector we got at that step.. and so on up the iterations till we got to back to the Q1. This way the matrices grow as you go back up. However unfortunately building Q as you work up the stack and after we have finished the reduction means you need to keep around all these intermediary reflector till you get the last one (the 1). Only then can we combine them.

Here I attempt an alternate solution that combines the reflectors as we go, starting with Q1. At each iteration of the reduction we got a new (first-column-reflector .. ) , and just like with S in the 2nd column case, we pad the matrix and make it n by n

\begin{equation} Q_{k} \\= \begin{bmatrix} I_{k-1,k-1} & 0\\ 0 & S_{n-k+1,n-k+1}\\ \end{bmatrix} \end{equation}

In core.matrix there is a convenient (block-diagonal-matrix .. ) function to handle making these

So far we've been looking at Q1Q2 .. QnA=R, but ultimately we want to get to A=QR. The reflectors are their own inverse, so written out like that the equation remains easily invertable. While by contrast Q-1A=R is not so easy to invert… b/c Q-1 may not be a reflector at all. So we flip the equation ahead of time A=Qn .. Q2Q1R and we make sure to build Q in the right order Q=Q1Q2 .. Q1

(defn- make-padded-reflector
"Make a reflector that leaves the first columns untouched"
(join-along 0
(join-along 1
(column-count reflector)))
(join-along 1
(zero-matrix (row-count reflector)
reflector))))
;; crazy code b/c block-diagonal-matrix code is broken
;; see: https://github.com/mikera/vectorz-clj/issues/70

(defn- reduce-to-qr
"Increase the dimension of a reflector by padding it with an identity matrix"
[reduction-matrix input-matrix]
(let [reflector (first-column-reflector input-matrix)
(row-count input-matrix))
reflector
reflector))]
(do (assign! input-matrix
(mmul reflector
input-matrix))
(if (or (= 1 (row-count input-matrix))
(= 1 (column-count input-matrix)))
reduction-matrix ;; base case
(recur (mmul reduction-matrix
(submatrix input-matrix
1
(dec (row-count input-matrix))
1
(dec (column-count input-matrix))))))))
(defn qr!
"A wrapper for the real function"
[input-matrix]
(reduce-to-qr (identity-matrix (row-count input-matrix))
input-matrix))


The function reduces the input matrix to R and returns Q.

(def A (mutable [[43.0 36.0 38.0 90.0]
[21.0 98.0 55.0 48.0]
[72.0 13.0 98.0 12.0]
[28.0 38.0 73.0 20.0]]))

(def Q (qr! A))
(pm Q)
;; [[0.473  0.073 -0.690 -0.543]
;;  [0.231  0.897 -0.041  0.374]
;;  [0.792 -0.384  0.149  0.450]
;;  [0.308  0.204  0.708 -0.602]]
(pm A)
;; [[90.874 61.690 130.830  69.349]
;;  [ 0.000 93.313  29.311  49.102]
;;  [-0.000  0.000  37.767 -48.089]
;;  [ 0.000 -0.000   0.000 -37.619]]
(pm (mmul Q A))
;; [[43.000 36.000 38.000 90.000]
;;  [21.000 98.000 55.000 48.000]
;;  [72.000 13.000 98.000 12.000]
;;  [28.000 38.000 73.000 20.000]]


Note that this all works perfectly well for fat (underdefined) and skinny (overdefined) matrices

(def A (mutable [[43.0 36.0 38.0]
[21.0 98.0 55.0]
[72.0 13.0 98.0]
[28.0 38.0 73.0]
[65.0 23.0 85.0]]))
(def Q (qr! A))
(pm Q)
;; [[0.385  0.122 -0.678 -0.578 -0.207]
;;  [0.188  0.907 -0.042  0.374 -0.024]
;;  [0.644 -0.295  0.170  0.373 -0.574]
;;  [0.251  0.233  0.713 -0.612 -0.025]
;;  [0.582 -0.147 -0.033  0.111  0.791]]
(pm A)
;; [[111.727 63.557 155.862]
;;  [ -0.000 94.882  30.090]
;;  [ -0.000  0.000  37.799]
;;  [  0.000 -0.000   0.000]
;;  [ -0.000  0.000   0.000]]
(pm (mmul Q A))
;; [[43.000 36.000 38.000]
;;  [21.000 98.000 55.000]
;;  [72.000 13.000 98.000]
;;  [28.000 38.000 73.000]
;;  [65.000 23.000 85.000]]

(def A (mutable [[43.0 36.0 38.0 90.0 54.0]
[21.0 98.0 55.0 48.0 92.0]
[72.0 13.0 98.0 12.0 47.0]]))
(def Q (qr! A))
(pm Q)
;;  [[0.497  0.108 -0.861]
;; [0.243  0.935  0.258]
;; [0.833 -0.337  0.439]]
(pm A)
;; [[86.452 52.538 113.878  66.418 88.349]
;;  [ 0.000 91.153  22.480  50.581 76.019]
;;  [-0.000  0.000  24.484 -59.818 -2.122]]
(pm (mmul Q A))
;; [[43.000 36.000 38.000 90.000 54.000]
;;  [21.000 98.000 55.000 48.000 92.000]
;;  [72.000 13.000 98.000 12.000 47.000]]


In a more sophisticated linear algebra system you may want to keep the resulting R matrix as two separate submatrices b/c upper diagonal matrices can have more performant code associated with them

Note: In the little testing I've done, my solution is not giving very accurate results

## Extended reduction

While the last section is the more flexible, we will see in Least Squares problem that it can actually make sense to just reduce both the linear systems A and the output-vector b with the same reflectors at the same time - and in so doing avoid making Q entirely. This is computationally favorable as well. So we just need to expend reduce-to-r to handle reducing an output vector in parallel.

(defn reduction
"Reduce an INPUT-MATRIX to a /lower triangular/ or /upper trapeziodal/ orthonormal matrix
and apply the reduction to the OUTPUT-VECTOR as well"
[input-matrix output-vector]
(let [reflector (first-column-reflector input-matrix)]
(assign! input-matrix
(mmul reflector
input-matrix))
(assign! output-vector
(mmul reflector
output-vector))
(if (or (= 1 (row-count input-matrix))
(= 1 (column-count input-matrix)))
input-matrix ;; base case
(recur (submatrix input-matrix
1
(dec (row-count input-matrix))
1
(dec (column-count input-matrix)))
(subvector output-vector
1
(dec (ecount output-vector)))))))

(let [A (mutable [[43.0 36.0 38.0 ]
[21.0 98.0 55.0 ]
[72.0 13.0 98.0 ]
[28.0 38.0 73.0 ]])
b (mutable [ 11.0 22.0 33.0 44.0 ])]
(reduction A b)
(pm A)
(pm b))
;; [[90.874 61.690 130.830]
;;  [ 0.000 93.313  29.311]
;;  [-0.000  0.000  37.767]
;;  [ 0.000 -0.000   0.000]]
;; [49.993 16.814 27.554 -9.424]
;; nil

(let [A (mutable [[1.0 0.0 0.0]
[1.0 0.25 0.0625]
[1.0 0.50 0.25]
[1.0 0.75 0.5625]
[1.0 1.00 1.0]])
b (mutable [0.000 0.008 0.015 0.019 0.02])]
(reduction A b)
(pm A) (pm b))