Module matrix 

Matrix operations from scratch.

Provides a Matrix type with arithmetic (add, sub, mul, scalar), transpose, determinant via Gaussian elimination, inverse, and linear system solving (Ax = b).

Structs

Cholesky

Cholesky decomposition A = L · Lᵀ for a symmetric positive-definite matrix. Holds the lower-triangular L factor.

EigenPair

A converged power-iteration result: (λ, v) where v is the eigenvector corresponding to the dominant eigenvalue λ.

Lu

A row-major LU decomposition of a square matrix.

Matrix

A row-major f64 matrix.

PowerIterOptions

Options for Matrix::power_iteration.

Svd

Singular Value Decomposition: A = U · Σ · Vᵀ for any m × n matrix (possibly rectangular). The columns of u are the left singular vectors, singular_values are the diagonal of Σ (in descending order), and the columns of v are the right singular vectors.