Preferred-orientation quantum graphs show spectral statistics that deviate from random matrix theory, and the deviations are explained by periodic orbit and Eulerian cycle combinatorics.
Linear Program-Based Stability Conditions for Nonlinear Autonomous Systems
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abstract
This paper introduces a novel approach to evaluating the asymptotic stability of equilibrium points in both continuous-time (CT) and discrete-time (DT) nonlinear autonomous systems. By utilizing indirect Lyapunov methods and linearizing system dynamics through Jacobian matrices, the methodology replaces traditional semi-definite programming (SDP) techniques with computationally efficient linear programming (LP) conditions. This substitution substantially lowers the computational burden, including time and memory usage, particularly for high-dimensional systems. The stability criteria are developed using matrix transformations and leveraging the structural characteristics of the system, improving scalability. Several examples demonstrated the computational efficiency of the proposed approach compared to the existing SDP-based criteria, particularly for high-dimensional systems.
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Spectral statistics of preferred orientation quantum graphs
Preferred-orientation quantum graphs show spectral statistics that deviate from random matrix theory, and the deviations are explained by periodic orbit and Eulerian cycle combinatorics.