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linear-algebra-practice

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Applied linear algebra — decompositions, least squares, conditioning, and numerical habits for real matrices.

skills/linear-algebra-practice/SKILL.md

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## Overview

Linear algebra is the computational substrate of science: every fit,
simulation, and data analysis reduces to matrix operations. This skill
covers choosing the right decomposition (LU, QR, SVD, eigendecomposition),
solving least-squares problems properly, understanding conditioning and
stability, and the numerical habits (never invert explicitly, exploit
structure) that separate working code from slow, wrong code.

## When to use

- Solving linear systems or least-squares problems
- Choosing between SVD, QR, eigendecomposition, Cholesky for a task
- Diagnosing numerical instability: huge condition numbers, rank deficiency
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