Fast Algorithms for Packing Proportional Fairness and Its Dual
Abstract
The proportional fair resource allocation problem is a major problem studied in flow control of networks, operations research, and economic theory, where it has found numerous applications. This problem, defined as the constrained maximization of $\sum_i \log x_i$, is known as the packing proportional fairness problem when the feasible set is defined by positive linear constraints and $x \in \mathbb{R}_{\geq 0}^n$. In this work, we present a distributed accelerated first-order method for this problem which improves upon previous approaches. We also design an algorithm for the optimization of its dual problem. Both algorithms are width-independent.
Cite
Text
Criado et al. "Fast Algorithms for Packing Proportional Fairness and Its Dual." Neural Information Processing Systems, 2022.Markdown
[Criado et al. "Fast Algorithms for Packing Proportional Fairness and Its Dual." Neural Information Processing Systems, 2022.](https://mlanthology.org/neurips/2022/criado2022neurips-fast/)BibTeX
@inproceedings{criado2022neurips-fast,
title = {{Fast Algorithms for Packing Proportional Fairness and Its Dual}},
author = {Criado, Francisco and Martinez-Rubio, David and Pokutta, Sebastian},
booktitle = {Neural Information Processing Systems},
year = {2022},
url = {https://mlanthology.org/neurips/2022/criado2022neurips-fast/}
}