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01 ·Linear Algebra·★ MEMBER·13 min read A Tour of Matrix Decompositions — When to Reach for LU, QR, Cholesky, or SVD A decomposition rewrites one awkward transformation as a product of parts you can actually work with: solve it (LU), solve it at half price (Cholesky), orthogonalize and fit (QR), or survive a broken matrix (SVD). Starting from why nobody computes an inverse, this piece builds up to a single table that tells you which one to reach for, plus the numpy that goes with it.