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High-Performance Variance-Covariance Matrix Construction Using an Uncentered Gram Formulation

Computation 2025-12-09 v2 Machine Learning Numerical Analysis Numerical Analysis

Abstract

Reichel (2025) defined the bariance as a pairwise-difference measure that can be rewritten in linear time using only scalar sums. We extend this idea to the covariance matrix by showing that the standard matrix expression involving the uncentered Gram matrix and a correction term is algebraically identical to the pairwise-difference definition while avoiding explicit centering. The computation then reduces to one outer product of dimension p-by-p and a single subtraction. Benchmarks in Python show clear runtime gains, especially when BLAS optimizations are absent. Optionally faster Gram-matrix routines such as RXTX (Rybin et al., 2025) further reduce overall cost.

Keywords

Cite

@article{arxiv.2511.08223,
  title  = {High-Performance Variance-Covariance Matrix Construction Using an Uncentered Gram Formulation},
  author = {Felix Reichel},
  journal= {arXiv preprint arXiv:2511.08223},
  year   = {2025}
}

Comments

17 pages, 9 figures, 1 table

R2 v1 2026-07-01T07:32:04.953Z