# Linear Algebra - Orthogonalization - Building an orthogonal set of generators

### Table of Contents

## 1 - About

Original stated goal: Find the projection of b orthogonal to the space V spanned by arbitrary vectors <math>{v_1} , \dots , {v_n}</math>

## 2 - Articles Related

## 3 - Computation

- Input: A list <math>{v_1} , \dots , {v_n}</math> of vectors over the reals
- output: A list of mutually orthogonal vectors <math>v_1^* , \dots , v_n^*</math> such that

<MATH>Span \{ v_1^* , \dots , v_n^* \} = Span \{v_1, \dots , v_n\}</MATH>

# output: a list of mutually orthogonal vectors. def orthogonalize(vlist): vstarlist = [] for v in vlist: # project orthogonal find the next orthogonal vector # and make iteratively a longer and longer list of mutually orthogonal vectors. vstarlist.append(project_orthogonal(v, vstarlist)) return vstarlist

where:

- the project orthogonal function is the high dimensional projection computation of mutually orthogonal vectors.

Lemma: Throughout the execution of orthogonalize, the vectors in vstarlist are mutually orthogonal.

Proof: by induction, using the fact that each vector added to vstarlist is orthogonal to all the vectors already in the list.

### 3.1 - Property

#### 3.1.1 - Order matters

If we run the procedure orthogonalize twice, once with a list of vectors and once with the reverse of that list, the output lists will not be the reverses of each other.

#### 3.1.2 - Matrix Equation has a matrix composed of mutually orthogonal vector and an upper triangle matrix

- First iteration, since the set <math>\{v_0\}</math> is trivially a set of mutually orthogonal vectors:
- <math>v^*_0 = v_0</math>
- or as matrix equation: <math>[v_1] = [v^*_1] [1]</math>

- Second Iteration, <math>v^*_1</math> is the the projection orthogonal of <math>v_1</math> to <math>v^*_0</math> then:
- <math>v_1 = v^*_1 + \sigma_{01} v^*_0</math>
- or as matrix equation: <math>[v_1] = \begin{bmatrix}v^*_0 & v^*_1 \end{bmatrix} \begin{bmatrix}\sigma_{01} \\ 1 \end{bmatrix}</math>

- Third iteration: <math>v^*_2</math> is the projection of <math>v_2</math> orthogonal to <math>v^*_0</math> and <math>v^*_1</math>:
- <math>v_2 = v^*_2 + (\sigma_{02} v^*_0 + \sigma_{12} v^*_1)</math>
- or as matrix equation: <math>[v_2] = \begin{bmatrix}v^*_0 & v^*_1 & v^*_2 \end{bmatrix} \begin{bmatrix}\sigma_{02} \\ \sigma_{12} \\ 1 \end{bmatrix}</math>

- After N Iterations, we will get this matrix equation (formed of two matrix) expressing original vectors in terms of starred vectors. It's not a matrix multiplication

<MATH> \begin{bmatrix} \begin{array}{r|r|r|r} \, \: \; \> & \\ \, \: \; \> & \\ \\ v_0 & v_1 & v_2 & \dots & v_n \ \\ \ \\ \end{array} \end{bmatrix} = \begin{bmatrix} \begin{array}{r|r|r|r} v^*_0 & v^*_1 & v^*_2 & \dots & v^*_n \end{array} \end{bmatrix} \begin{bmatrix} 1 & \sigma_{01} & \sigma_{02} & \dots & \sigma_{0n} \\ & 1 & \sigma_{12} & \dots & \sigma_{1n} \\ & & 1 & \dots & \sigma_{2n} \\ & & & \ddots & \vdots \\ & & & & 1 \\ \end{bmatrix} </MATH>

The two matrices on the right are special:

- Columns of first one are mutually orthogonal.
- Second is upper triangular.