*Generally, what you find here is a mix of learning directly from the
book and the results of conversations I had with ChatGPT.*

*The goal of this exploration is to arrive at an AI/ML proficiency via
first principles.*

Linear Algebra Fundamentals [dsp-8M1W]

I have never studied linear algebra at all, so we’re going back to the beginning. Each header below are parts that I had to think hard about, so it may skip over stuff.

# Matrix Multiplication

To multiply two matrices \(A\) and \(B\), \(B\) must have the same number of rows as \(A\) has columns. For the equation \(A \times B = C\), each index in \(C\) is determined by:

\[ C_{i, j} = \sum_k A_{i, k} B_{k, j} \]

Where \(k\) is that matched rows in \(B\) and columns in \(A\)—which you can see in the equation above—is what we’re iterating over, respectively.

Thinking about this by indices in \(C\) was the missing link for anytime I’ve tried to learn this in the past, it feels like one of things that’s obvious to everyone who knows it already.

# Eigendecomposition

Not my first run-in with the Eigen-family of mathematical objects, but it is my first time to buckle in and figure out how and why exactly they work. The book sets this up by talking about representation and gives the example of how an integer, whether represented in binary or base-10, can still be represented via its prime factors. Eigendecomposition is purportedly a way to represent matrices via their functional properties.

The core idea is this: The eigendecomposition of a matrix \(A\) is the factorization \(A = A \Lambda A^{-1}\), where \(\Lambda\) is a diagonal matrix containing the eigenvalues of \(A\).

An eigenvalue has a corresponding vector \(\mathbf{v}\) with respect to \(A\) s.t. \(A \mathbf{v} = \lambda \mathbf{v}\) where \(\lambda\) is the eigenvalue corresponding to the vector \(\mathbf{v}\).

To compute Eigendecomposition we first find the eigenvalues and vectors.

For the following examples, we’ll use

\[ A = \begin{pmatrix} 4 & 1 \\ 2 & 3 \end{pmatrix} \]

## Eigenvalues

Solve \(\operatorname{det}(A - \lambda I) = 0\), the results are the eigenvalues. \(I\) is the identity matrix. The identity matrix is a square matrix of a specific size which has 1’s along its diagonal and 0’s elsewhere.

### Determinates

\(\operatorname{det}\) is a *determinate*. For 2x2 matrices of the form

\[ \begin{pmatrix} a & b \\ c & d \end{pmatrix} \]

\(\operatorname{det}\) is simply \(ad - bc\), but apparently for larger matrices, computing the determinate is much more complicated.

- Compute the matrix difference, which is done elementwise. \(A - \lambda I\) becomes

\[ \begin{pmatrix} \lambda 1 & \lambda 0 \\ \lambda 0 & \lambda 1 \end{pmatrix} = \begin{pmatrix} \lambda & 0 \\ 0 & \lambda \end{pmatrix} \]

so we end up with

\[ \begin{pmatrix} 4 - \lambda & 1 - 0 \\ 2 - 0 & 3 - \lambda \end{pmatrix} = \begin{pmatrix} 4 - \lambda & 1 \\ 2 & 3 - \lambda \end{pmatrix} \]

- Then compute the determinate

\[ \operatorname{det}(\begin{pmatrix} 4 - \lambda & 1 \\ 2 & 3 - \lambda \end{pmatrix}) = (4 - \lambda)(3 - \lambda) - (2 \times 1) = (4 - \lambda)(3 - \lambda) - 2 \]

Which we FOIL-ify to

\[ 12 - 4\lambda - 3\lambda + \lambda^2 - 2 \]

Yielding us the polynomial (recall that in computing the eigenvalues, \(\operatorname{det}(A - \lambda I) = 0\)).

\[ \lambda^2 - 7\lambda + 10 = 0 \]

The solutions to this polynomial are the eigenvalues. We factor into

\[ (\lambda - 5)(\lambda - 2) = 0 \]

Because \(-5 \times -2 = 10\) and \(-5 + -2 = -7\), which we can then
isolate individually to get \(\lambda - 5 = 0\) so \(\lambda = 5\) and
\(\lambda - 2 = 0\) so \(\lambda = 2\). **These are our eigenvalues**.

## Eigenvectors

For each eigenvalue \(\lambda_i\), we solve \((A - \lambda_i I) \mathbf{v} = \mathbf{0}\)I’m using bold face here on \(\mathbf{0}\) to denote the zero
vector.

.

Continuing with our example, first we solve with \(\lambda = 5\):

To start

\[ 5I = \begin{pmatrix} 5 & 0 \\ 0 & 5 \end{pmatrix} \]

Then

\[ \begin{pmatrix} 4 & 1 \\ 2 & 3 \end{pmatrix} - \begin{pmatrix} 5 & 0 \\ 0 & 5 \end{pmatrix} = \begin{pmatrix} -1 & 1 \\ 2 & -2 \end{pmatrix} \]

So we’re solving

\[ \begin{pmatrix} -1 & 1 \\ 2 & -2 \end{pmatrix} \begin{pmatrix} v_1 \\ v_2 \end{pmatrix} = \begin{pmatrix} 0 \\ 0 \end{pmatrix} \]

Which we can decompose (possibly a poor word choice) into

\[ -1v_1 + 1v_2 = 0 \\ 2v_1 - 2v_2 = 0 \]

In both cases, we end up with a case s.t. \(v_1 = v_2\), so “the simplest option” is “chosen”, yielding us the eigenvector

\[ \begin{pmatrix} 1 \\ 1 \end{pmatrix} \]

with respect to \(\lambda = 5\).