Abstract
Symmetric alternating direction method of multipliers (ADMM) is an efficient method for solving a class of separable convex optimization problems. This method updates the Lagrange multiplier twice with appropriate step sizes at each iteration. However, such step sizes were conservatively shrunk to guarantee the convergence in recent studies. In this paper, we are devoted to seeking larger step sizes whenever possible. The logarithmic-quadratic proximal (LQP) terms are applied to regularize the symmetric ADMM subproblems, allowing the constrained subproblems to then be converted to easier unconstrained ones. Theoretically, we prove the global convergence of such LQP-based symmetric ADMM by specifying a larger step size domain. Moreover, the numerical results on a traffic equilibrium problem are reported to demonstrate the advantage of the method with larger step sizes.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Journal of the Operations Research Society of China
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
