Introduction
Eigendecomposition only works for some square matrices. The singular value decomposition (SVD) works for every matrix, of any shape. It says that any linear map is just a rotation, a scaling along the axes, and another rotation. It is often called the "fundamental theorem of linear algebra" in practice, and powers PCA, recommender systems, compression, pseudo-inverses and LoRA.