Eigenvalues and Eigenvectors
Eigenvalues and eigenvectors are the most powerful tools in Linear Algebra.
We’ve talked about matrices and linear transformations in the previous article in the context of solving systems of equations, but there are often many cases where we have a linear transformation of a space to itself. In cases like that, we can repeatedly apply the linear transformation.
Repeatedly applying linear transformations can be quite time consuming. Furthermore, while the columns of a matrix indicate where we send the basis vectors, it’s difficult to interpret what’s going on with the matrices. The solution to both of these problems involves rewriting everything in the eigenbasis.
Prerequisites
While this is part of a series, you don’t need to know anything from the previous articles except what was covered in the previous article.
Motivation
While I’ve given some general motivation in the intro to the article, the motivation will become more clear if we give some examples.
Markov Chains
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