# Distributed Resource Allocation via ADMM over Digraphs

W. Jiang, M. Doostmohammadian, T. Charalambous

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

## Abstract

In this paper, we solve the resource allocation problem over a network of agents, with edges as communication links that can be unidirectional. The goal is to minimize the sum of allocation cost functions subject to a coupling constraint in a distributed way by using the finite-time consensus-based alternating direction method of multipliers (ADMM) technique. In contrast to the existing gradient descent (GD) based distributed algorithms, our approach can be applied to non-differentiable cost functions. Also, the proposed algorithm is initialization-free and converges at a rate of $\mathcal{O}\left( {1/k} \right)$, where k is the optimization iteration counter. The fast convergence performance related to iteration counter k compared to state-of-the-art GD based algorithms is shown via a simulation example.
Original language English 2022 IEEE 61st Conference on Decision and Control (CDC) IEEE 5645-5651 7 978-1-6654-6761-2 https://doi.org/10.1109/CDC51059.2022.9993326 Published - 10 Jan 2023 A4 Article in a conference publication IEEE Conference on Decision and Control - Cancun, MexicoDuration: 6 Dec 2022 → 9 Dec 2022Conference number: 61

### Conference

Conference IEEE Conference on Decision and Control CDC Mexico Cancun 06/12/2022 → 09/12/2022

## Keywords

• Couplings
• Directed graphs
• Linear programming
• Cost function
• Convex functions
• Large-scale systems
• Resource management
• Distributed optimization