A package for efficiently computing with symmetric extended generator representable semiseparable matrices (EGRSS Matrices) and a variant with added diagonal terms. In short this means matrices of the form

$$ \mathbf{K} = \text{\textbf{tril}}(\mathbf{UV}^\top) + \text{\textbf{triu}}\left(\mathbf{VU}^\top,1\right), \quad \mathbf{U}, \mathbf{V}\in\mathbb{R}^{n\times p} $$

$$ \mathbf{K} = \text{\textbf{tril}}(\mathbf{UV}^\top) + \text{\textbf{triu}}\left(\mathbf{VU}^\top,1\right) + d \mathbf{I}, \quad \mathbf{U}, \mathbf{V}\in\mathbb{R}^{n\times p},\ d\in\mathbb{R} $$

$$ \mathbf{K} = \text{\textbf{tril}}(\mathbf{UV}^\top) + \text{\textbf{triu}}\left(\mathbf{VU}^\top,1\right) + \text{\textbf{diag}}(\mathbf{d}), \quad \mathbf{U}, \mathbf{V}\in\mathbb{R}^{n\times p},\ \mathbf{d}\in\mathbb{R}^n $$

All implemented algorithms (multiplication, Cholesky factorization, forward/backward substitution as well as various traces and determinants) scales with $O(p^kn)$. Since $p \ll n$ this result in very scalable computations.

A more in-depth descriptions of the algorithms can be found in [1] or here.

An application of EGRSS matrices is smoothing splines as the so-called spline kernel matrix is an EGRSS matrix. An example implementation can be found in examples/smoothingsplines.py.

[1] M. S. Andersen and T. Chen, “Smoothing Splines and Rank Structured Matrices: Revisiting the Spline Kernel,” SIAM Journal on Matrix Analysis and Applications, 2020.