AI engineering glossary · Linear algebra
What is cholesky decomposition?
Cholesky decomposition: A factorisation of a symmetric positive definite matrix into a lower triangular matrix times its own transpose. It takes about half the work of general elimination and is used to solve equations and sample from multivariate normal distributions.
Cholesky decomposition: an example
For [[4,2],[2,3]], L = [[2,0],[1,1.414]] and L times L transposed reproduces the matrix.
Related terms
More Linear algebra terms
- Column space
- Column vector
- Cosine similarity
- Covariance matrix
- Cross product
- Dense matrix
- Design matrix
- Determinant
Where you learn it
Cholesky decomposition is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.