56 research outputs found
Semi-Global Exponential Stability of Augmented Primal-Dual Gradient Dynamics for Constrained Convex Optimization
Primal-dual gradient dynamics that find saddle points of a Lagrangian have
been widely employed for handling constrained optimization problems. Building
on existing methods, we extend the augmented primal-dual gradient dynamics
(Aug-PDGD) to incorporate general convex and nonlinear inequality constraints,
and we establish its semi-global exponential stability when the objective
function is strongly convex. We also provide an example of a strongly convex
quadratic program of which the Aug-PDGD fails to achieve global exponential
stability. Numerical simulation also suggests that the exponential convergence
rate could depend on the initial distance to the KKT point
Stability and Regret bounds on Distributed Truncated Predictive Control for Networked Dynamical Systems
This work is primarily concerned about the distributed control of networked
linear timeinvariant (LTI) systems. In particular, we propose a truncated
predictive control algorithm based on -hop neighbourhoods of the agents
of the networked system. We establish stability and regret bounds for the
proposed algorithm, which shows that the regret decays exponentially when the
temporal prediction horizon and the spatial radius increases.Comment: 25 pages, 2 figures, submitted to ACC 202
Finite-Time Analysis of Asynchronous Stochastic Approximation and Q-Learning
We consider a general asynchronous Stochastic Approximation (SA) scheme featuring a weighted infinity-norm contractive operator, and prove a bound on its finite-time convergence rate on a single trajectory. Additionally, we specialize the result to asynchronou
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