22 research outputs found
Solving specified-time distributed optimization problem via sampled-data-based algorithm
Despite significant advances on distributed continuous-time optimization of
multi-agent networks, there is still lack of an efficient algorithm to achieve
the goal of distributed optimization at a pre-specified time. Herein, we design
a specified-time distributed optimization algorithm for connected agents with
directed topologies to collectively minimize the sum of individual objective
functions subject to an equality constraint. With the designed algorithm, the
settling time of distributed optimization can be exactly predefined. The
specified selection of such a settling time is independent of not only the
initial conditions of agents, but also the algorithm parameters and the
communication topologies. Furthermore, the proposed algorithm can realize
specified-time optimization by exchanging information among neighbours only at
discrete sampling instants and thus reduces the communication burden. In
addition, the equality constraint is always satisfied during the whole process,
which makes the proposed algorithm applicable to online solving distributed
optimization problems such as economic dispatch. For the special case of
undirected communication topologies, a reduced-order algorithm is also
designed. Finally, the effectiveness of the theoretical analysis is justified
by numerical simulations
Dynamic and Distributed Online Convex Optimization for Demand Response of Commercial Buildings
We extend the regret analysis of the online distributed weighted dual
averaging (DWDA) algorithm [1] to the dynamic setting and provide the tightest
dynamic regret bound known to date with respect to the time horizon for a
distributed online convex optimization (OCO) algorithm. Our bound is linear in
the cumulative difference between consecutive optima and does not depend
explicitly on the time horizon. We use dynamic-online DWDA (D-ODWDA) and
formulate a performance-guaranteed distributed online demand response approach
for heating, ventilation, and air-conditioning (HVAC) systems of commercial
buildings. We show the performance of our approach for fast timescale demand
response in numerical simulations and obtain demand response decisions that
closely reproduce the centralized optimal ones
A geometrically converging dual method for distributed optimization over time-varying graphs
In this paper we consider a distributed convex optimization problem over
time-varying undirected networks. We propose a dual method, primarily averaged
network dual ascent (PANDA), that is proven to converge R-linearly to the
optimal point given that the agents objective functions are strongly convex and
have Lipschitz continuous gradients. Like dual decomposition, PANDA requires
half the amount of variable exchanges per iterate of methods based on DIGing,
and can provide with practical improved performance as empirically
demonstrated.Comment: Submitted to Transactions on Automatic Contro