cvx-linalg 0.4.1

Linear algebra utilities for portfolio optimization

cvx-linalg

Linear algebra utilities for portfolio optimization, part of the jebel-quant ecosystem.

Installation

pip install cvx-linalg

Usage

from cvx.linalg import a_norm, cholesky, ewm_covariance, inv_a_norm, pca, rand_cov, solve, valid

Functions

• a_norm(vector, matrix=None) — Euclidean norm or NaN-aware matrix norm
• cholesky(cov) — Upper triangular Cholesky factor R such that R.T @ R = cov
• ewm_covariance(data, assets, index_col, window, is_halflife, warmup) — Exponentially weighted covariance matrices from a Polars DataFrame
• inv_a_norm(vector, matrix=None) — Euclidean norm or inverse NaN-aware matrix norm
• pca(returns, n_components) — Principal Component Analysis via SVD
• rand_cov(n, seed) — Random positive semi-definite covariance matrix
• solve(matrix, rhs) — Solve a linear system after removing invalid rows/columns
• valid(matrix) — Extract valid submatrix by removing rows/columns with non-finite diagonal entries