Singular value decomposition of complexes
Danielle A. Brake, Jonathan D. Hauenstein, Frank-Olaf Schreyer, Andrew J. Sommese, Michael E. Stillman
arXiv:1804.098386 citationsh-index: 49
AI Analysis
This work introduces a new mathematical tool for analyzing complexes of vector spaces, potentially benefiting fields like topology and data analysis, but the abstract lacks empirical validation or comparison to existing methods.
The paper extends singular value decomposition (SVD) to finite complexes of real vector spaces, providing two computational methods and demonstrating applications. No concrete numerical results are reported.
Singular value decompositions of matrices are widely used in numerical linear algebra with many applications. In this paper, we extend the notion of singular value decompositions to finite complexes of real vector spaces. We provide two methods to compute them and present several applications.