Performant Tridiagonal Factorization of Skew-Symmetric Matrices
Ishna Satyarth, Chao Yin, Devin A. Matthews, Maggie Myers, Robert van de Geijn, RuQing G. Xu

TL;DR
This paper introduces a high-performance method for tridiagonal factorization of skew-symmetric matrices, enabling efficient determinant computation and system solving, with optimized parallel CPU implementations leveraging BLIS capabilities.
Contribution
It develops a novel, efficient algorithm for skew-symmetric matrix factorization into LTL^T form, utilizing FLAME methodology and BLIS enhancements for superior performance.
Findings
Achieves significantly faster performance than previous algorithms.
Demonstrates effective parallel CPU implementations with reduced memory traffic.
Provides a prototype C++ API for correct-by-construction algorithm translation.
Abstract
The factorization of skew-symmetric matrices is a critically understudied area of dense linear algebra, particularly in comparison to that of general and symmetric matrices. While some algorithms can be adapted from the symmetric case, the cost of algorithms can be reduced by exploiting skew-symmetry. This work examines the factorization of a skew-symmetric matrix into its decomposition, where is unit lower triangular and is tridiagonal. This is also known as a triangular tridiagonalization. This operation is a means for computing the determinant of as the square of the (cheaply-computed) Pfaffian of the skew-symmetric tridiagonal matrix as well as for solving systems of equations, across fields such as quantum electronic structure and machine learning. Its application also often requires pivoting in order to improve numerical stability. We compare and…
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