# Accelerated Distributed Allocation

arXiv: https://arxiv.org/abs/2401.15598 · HTML: https://mlsanity.com/paper/2401.15598/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2024.

## Built on by (most cited first, 4 of 4)

- [Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression](https://mlsanity.com/paper/2506.18748/) (2025)
- [Logarithmically Quantized Distributed Optimization over Dynamic Multi-Agent Networks](https://mlsanity.com/paper/2410.20345/) (2024)
- [Machine Learning and CPU (Central Processing Unit) Scheduling Co-Optimization over a Network of Computing Centers](https://mlsanity.com/paper/2510.25176/) (2025)
- [Using Non-Lipschitz Signum-based Functions for Distributed Optimization and Machine Learning: Trade-off Between Con-vergence Rate and Optimality Gap](https://mlsanity.com/paper/2608.01220/) (2026)

## Abstract (arXiv)

Distributed allocation finds applications in many scenarios including CPU scheduling, distributed energy resource management, and networked coverage control. In this paper, we propose a fast convergent optimization algorithm with a tunable rate using the signum function. The convergence rate of the proposed algorithm can be managed by changing two parameters. We prove convergence over uniformly-connected multi-agent networks. Therefore, the solution converges even if the network loses connectivity at some finite time intervals. The proposed algorithm is all-time feasible, implying that at any termination time of the algorithm, the resource-demand feasibility holds. This is in contrast to asymptotic feasibility in many dual formulation solutions (e.g., ADMM) that meet resource-demand feasibility over time and asymptotically.