Special Issue: ICCAS 2024

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

A Generalized Primal-dual Correction Method for Saddle-point Problems With a Nonlinear Coupling Operator

Sai Wang and Yi Gong*

Southern University of Science and Technology

Abstract

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.

Article

Special Issue: ICCAS 2024

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.

A Generalized Primal-dual Correction Method for Saddle-point Problems With a Nonlinear Coupling Operator

Sai Wang and Yi Gong*

Southern University of Science and Technology

Abstract

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.

IJCAS
February 2025

Vol. 23, No. 2, pp. 359~682

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IJCAS

eISSN 2005-4092
pISSN 1598-6446