QR factorization:

We can rewrite this equation as:

Interpretation: apply an orthogonal transformation to make the matrix triangular = orthogonal triangularization.

Start with 1st column.

  • We need to apply an orthogonal transformation that zeros out all entries except the first.
  • Orthogonal transformations are either rotations or reflections.
  • It turns out that reflections are easier to apply than rotations.

Orthogonal transformations are either rotations or reflections:

We want to find a reflection that sends the first column to the axis:

These are the steps:

From this figure, we can derive the key definitions:

  • Vector for projection: .
  • Reflection matrix: with

This completely defines the Householder transformation .

Properties:

  • We can check that . In fact: and .
  • We can check that .

QR factorization