Published

Distributed Optimal Resource Allocation Using Transformed Primal-Dual Method

Solmaz S. Kia, Jingrong Wei, and Long Chen

2023 American Control Conference (ACC)

PDF   Bibtex   doi: 10.23919/ACC55779.2023.10156601

ABSTRACT:

We consider an in-network optimal resource allocation problem
in which a group of agents interacting over a connected graph want to
meet a demand while minimizing their collective cost. The contribution
of this paper is to design a distributed continuous-time algorithm for
this problem inspired by a recently developed first-order transformed
primal-dual method. The solution applies to cluster-based setting
where each agent may have a set of subagents, and its local cost is
the sum of the cost of these subagents. The proposed algorithm
guarantees an exponential convergence for strongly convex costs and
asymptotic convergence for convex costs. Exponential convergence when
the local cost functions are strongly convex is achieved even when the
local gradients are only locally Lipschitz. For convex local cost
functions, our algorithm guarantees asymptotic convergence to a point
in the minimizer set. Through numerical examples, we show that our
proposed algorithm delivers a faster convergence compared to existing
distributed resource allocation algorithms.