International Journal of Control, Automation, and Systems 2025; 23(2): 638-645
https://doi.org/10.1007/s12555-024-0453-8
© The International Journal of Control, Automation, and Systems
The saddle-point problems (SPPs) with nonlinear coupling operators frequently arise in various control systems, such as dynamic programming optimization, H-infinity control, and Lyapunov stability analysis. However, traditional primal-dual methods are constrained by fixed regularization factors. In this paper, a novel generalized primal-dual correction method (GPD-CM) is proposed to adjust the values of regularization factors dynamically. It turns out that this method can achieve the minimum theoretical lower bound of regularization factors, allowing for larger step sizes under the convergence condition being satisfied. The convergence of the GPD-CM is directly achieved through a unified variational framework. Theoretical analysis shows that the proposed method can achieve an ergodic convergence rate of O(1/t). Numerical results support our theoretical analysis for an SPP with an exponential coupling operator.
Keywords Nonlinear optimization, prediction-correction method, saddle-point problem, variational analysis.
International Journal of Control, Automation, and Systems 2025; 23(2): 638-645
Published online February 1, 2025 https://doi.org/10.1007/s12555-024-0453-8
Copyright © The International Journal of Control, Automation, and Systems.
Sai Wang and Yi Gong*
Southern University of Science and Technology
The saddle-point problems (SPPs) with nonlinear coupling operators frequently arise in various control systems, such as dynamic programming optimization, H-infinity control, and Lyapunov stability analysis. However, traditional primal-dual methods are constrained by fixed regularization factors. In this paper, a novel generalized primal-dual correction method (GPD-CM) is proposed to adjust the values of regularization factors dynamically. It turns out that this method can achieve the minimum theoretical lower bound of regularization factors, allowing for larger step sizes under the convergence condition being satisfied. The convergence of the GPD-CM is directly achieved through a unified variational framework. Theoretical analysis shows that the proposed method can achieve an ergodic convergence rate of O(1/t). Numerical results support our theoretical analysis for an SPP with an exponential coupling operator.
Keywords: Nonlinear optimization, prediction-correction method, saddle-point problem, variational analysis.
Vol. 23, No. 2, pp. 359~682