Continuous-Time Varying Complex QR Decomposition via Zeroing Neural Dynamics - Vasilios N. Katsikis; Spyridon D. Mourtas; Predrag S. Stanimirović; Zhang, Yunong (Neural Processing Letters, 2021)
QR decomposition (QRD) is of fundamental importance for matrix factorization in both real and complex cases. In this paper, by using zeroing neural dynamics method, a continuous-time model is proposed for solving the time-varying problem of QRD in real-time. The proposed dynamics use time derivative information from a known real or complex matrix. Furthermore, its theoretical analysis is provided to substantiate the convergence and effectiveness of solving the time-varying QRD problem. In addition, numerical experiments in four different-dimensional time-varying matrices show that the proposed model is effective for solving the time-varying QRD problem both in the case of a real or a complex matrix as input.