2025/03/04 by Wang, Ying, Ephremidze, Lasha, Reyes, Ronaldo Garcıa +1
#47A68 #Complex Variables (math.CV) #FOS: Mathematics
paper · doi:10.48550/arxiv.2503.02553
Spectral factorization is a powerful mathematical tool with diverse applications in signal processing and beyond. The Janashia-Lagvilava method has emerged as a leading approach for matrix spectral factorization. In this paper, we extend a central equation of the method to the non-commutative case, enabling polynomial coefficients to be represented in block matrix form while preserving the equation's fundamental structure. This generalization results in an exponential speedup for high-dimensional matrices. Our approach addresses challenges in factorizing massive-dimensional matrices encountered in neural data analysis and other practical applications.