Eigenvectors and eigenvalues are fundamental concepts in linear algebra, crucial for diverse fields such as machine learning, physics, and computer graphics. But what are they?
Simply put, an eigenvector of a square matrix is a non-zero vector that only changes by a scalar factor when that linear transformation is applied. The scalar is known as the eigenvalue associated with that eigenvector.
Mathematically, for a matrix A and an eigenvector v with eigenvalue λ, we express it as:
A * v = λ * v
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