Theory of Intelligence
MATH FOR AI•Linear Algebra•Eigenvalues And Eigendecomposition•10.3 Cholesky Decomposition

Introduction

Symmetric positive definite matrices revisited, the Cholesky factor L·Lᵀ, and its uses in ML.

Introduction

For positive numbers we can take square roots: 9=3⋅39 = 3\cdot3. Symmetric positive definite matrices have an equivalent — the Cholesky decomposition A=LL⊤A = LL^\top, with LL lower triangular. It is about twice as fast as general elimination and is the standard way to work with covariance matrices: sampling from Gaussians, computing log-determinants, and solving systems in Gaussian processes.

Discuss theoretical concepts, mathematical formulas, and architectural foundations.

Supports inline code with `backticks`
No comments yet. Be the first to start the discussion!