Decentralized Online Convex Optimization in Networked Systems
Authors: Yiheng Lin, Judy Gan, Guannan Qu, Yash Kanoria, Adam Wierman
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We show that LPC achieves a competitive ratio of 1 + O(ρk T ) + O(ρr S) in an adversarial setting, where ρT and ρS are constants in (0, 1) that increase with the relative strength of temporal and spatial interaction costs, respectively. This is the first competitive ratio bound on decentralized predictive control for networked online convex optimization. Further, we show that the dependence on k and r in our results is near optimal by lower bounding the competitive ratio of any decentralized online algorithm. |
| Researcher Affiliation | Academia | 1Department of Computing and Mathematical Sciences, California Institute of Technology 2Decision, Risk, and Operations division, Columbia Business School 3Department of Electrical and Computer Engineering, Carnegie Mellon University. |
| Pseudocode | Yes | Algorithm 1 Localized Predictive Control (for agent v) |
| Open Source Code | No | The paper does not contain any statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not report on experiments using a dataset. The multiproduct pricing section is a theoretical example of application, not an empirical evaluation using data. |
| Dataset Splits | No | The paper is theoretical and does not report on experiments using dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any computational experiments that would require specific hardware. |
| Software Dependencies | No | The paper is theoretical and does not describe the implementation of any software, thus no software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup, hyperparameters, or training configurations. |