Add coverage of non-diagonalisable matrices and the Jordan form (and its effect on linear state-space transients)

Nothing in the lecture repos covers what happens when a matrix is not diagonalisable. That is a real hole rather than a missing nicety: eigen_I.md in lecture-python-intro teaches eigenvalues, eigenvectors and power iteration, and uses the word "diagonalizable" exactly once, in passing, without ever saying when diagonalisability fails or what to do instead. linear_algebra.md here does not treat it at all. So a reader who meets a defective matrix has nowhere to go.

The natural application is linear state-space dynamics. linear_models.md develops the model, its moments and its stability without an eigendecomposition treatment, so the case where $A = P \Lambda P^{-1}$ does not exist is simply absent — even though it changes the form of the transient: repeated eigenvalues with deficient eigenvectors produce polynomial-times-exponential terms rather than pure exponentials.

This was requested in QuantEcon/meta#28, Tom Sargent's brief on eigenvalues and eigenfunctions, under "Linear state-space dynamics — cases where we require Jordan form", which he noted was "dedicated to Lars Hansen who often raises this case". That issue has been closed against the material that did ship; this is one of the residuals it did not cover.

Suggested shape, in rough order of dependency:

• when diagonalisation fails — algebraic versus geometric multiplicity, and what "defective" means
• the Jordan normal form as the general replacement, with the simplest non-trivial block worked explicitly
• the consequence for dynamics: how a Jordan block changes the transient in a linear state-space model, relative to the diagonalisable case already covered

Worth deciding as part of picking this up: whether the multiplicity material belongs here or as a short addition to eigen_I in lecture-python-intro, with the state-space consequence staying in this repo. The brief filed it under state-space dynamics, which is why it is proposed here.

Start by comparing eigen_I.md in lecture-python-intro with linear_algebra.md and linear_models.md in this repository, following how diagonalisation and state-space dynamics are currently introduced. Done means the lecture material explains defective matrices, algebraic and geometric multiplicity, Jordan form, and the polynomial-times-exponential transient consequence, with the placement of the multiplicity discussion decided.