Online convex optimization for cumulative constraints
Authors: Jianjun Yuan, Andrew Lamperski
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In numerical experiments, we show that our algorithm closely follows the constraint boundary leading to low cumulative violation. |
| Researcher Affiliation | Academia | Jianjun Yuan Department of Electrical and Computer Engineering University of Minnesota Minneapolis, MN, 55455 yuanx270@umn.edu; Andrew Lamperski Department of Electrical and Computer Engineering University of Minnesota Minneapolis, MN, 55455 alampers@umn.edu |
| Pseudocode | Yes | Algorithm 1 Generalized Online Convex Optimization with Long-term Constraint |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-sourcing of their code. |
| Open Datasets | Yes | The demand dt is adapted from real-world 5-minute interval demand data between 04/24/2018 and 05/03/2018 1, which is shown in Fig.3(a). The footnote 1 links to: https://www.iso-ne.com/isoexpress/web/reports/load-and-demand |
| Dataset Splits | No | The paper does not explicitly provide specific training/test/validation dataset splits, percentages, or absolute sample counts needed for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or cloud instances) used for running its experiments. |
| Software Dependencies | No | The paper mentions using "CVXPY [5]" and "SAGA [4]" but does not specify their version numbers or other software dependencies with version information. |
| Experiment Setup | Yes | Throughout the experiments, our algorithm has the following fixed parameters: α = 0.5, σ = (m+1)G2 / (2(1 − α)) , η = R(m+1). In the economic dispatch example, parameters are specified: a1 = 0.2, a2 = 0.12, a3 = 0.14, b1 = 1.5, b2 = 1, b3 = 0.6, d1 = 0.26, d2 = 0.38, d3 = 0.37, Emax = 100, ξ = 0.5, and x1,max = 20, x2,max = 15, x3,max = 18. |