Overview

Gram-Schmidt orthogonalization is an algorithm that takes in a basis $\left\{b_i\right\}_{i=1}^n$ as an input and returns a basis $\left\{b_i^*\right\}_{i=1}^n$where all vectors are orthogonal, i.e. at right angles. This new basis is defined as

where $\mu_{i,j}$is the Gram-Schmidt coefficients.

One can immediately check that this new basis is orthogonal, meaning

Let $\mathcal B$be the matrix where the $i$th row is given by $b_i$and$\mathcal B^*$be the matrix where the $i$th row is given by $b_i^*$, then the Gram-Schmidt orthogonalization gives us $\mathcal B=\mu\mathcal B^*$where $\mu_{i,i}=1,\mu_{j,i}=0$and $\mu_{i,j}$is the Gram-Schmidt coefficient. As an example, consider the basis of a subspace of $\mathbb R^4$:

Instead of doing the Gram-Schmidt orthogonalization by hand, we can get sage to do it for us:

B = Matrix([
[-1, -2, 3, 1],
[-6, -4, 5, 1],
[5, 5, 1, -3]])
​
B.gram_schmidt()

This outputs two matrices, $\mathcal B^*$and $\mu$:

(
[-1 -2  3  1]  [ 1  0  0]
[-4  0 -1 -1]  [ 2  1  0]
[ 0  3  3 -3], [-1 -1  1]
)

One can quickly verify that $\mathcal B=\mu\mathcal B^*$ and that the rows of $\mathcal B^*$are orthogonal to each other.

A useful result is that

Intuitively, this tells us that the more orthogonal a set of basis for a lattice is, the shorter it is as the volume must be constant.

Exercises

1) Show that the basis $b_i^*$is orthogonal.

2) Verify that the output of sage is indeed correct.

3) Show that $\mu\mu^T=1$and $\mathcal B^*\mathcal B^{*T}$ is a diagonal matrix whose entries are $\left\lVert b_i^*\right\rVert$. Conclude that $\det\left(\mathcal B\mathcal B^T\right)=\det\left(\mathcal B^*\mathcal B^{*T}\right)=\prod_i\left\lVert b_i^*\right\rVert$.

4*) Given the Iwasawa decomposition $\mathcal B=LDO$where $L$is a lower diagonal matrix with $1$on its diagonal, $D$is a diagonal matrix and $O$an orthogonal matrix, meaning $OO^T=1$, show that $\mathcal B^*=DO$and $\mu=L$. Furthermore, prove that such a decomposition is unique.