Distributed and time-varying primal-dual dynamics via contraction analysis
arXiv:2003.12665
Abstract
In this note, we provide an overarching analysis of primal-dual dynamics associated to linear equality-constrained optimization problems using contraction analysis. For the well-known standard version of the problem: we establish convergence under convexity and the contracting rate under strong convexity. Then, for a canonical distributed optimization problem, we use partial contractivity to establish global exponential convergence of its primal-dual dynamics. As an application, we propose a new distributed solver for the least-squares problem with the same convergence guarantees. Finally, for time-varying versions of both centralized and distributed primal-dual dynamics, we exploit their contractive nature to establish bounds on their tracking error. To support our analyses, we introduce novel results on contraction theory.
References in corpus (4)
- Stable Concurrent Synchronization in Dynamic System Networks
- Input-Output Performance of Linear-Quadratic Saddle-Point Algorithms with Application to Distributed Resource Allocation Problems
- Exponential Stability of Primal-Dual Gradient Dynamics with Non-Strong Convexity
- Contraction Analysis on Primal-Dual Gradient Optimization