Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Online convex optimization for cumulative constraints
Authors: Jianjun Yuan, Andrew Lamperski
NeurIPS 2018 | Venue PDF | 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 EMAIL; Andrew Lamperski Department of Electrical and Computer Engineering University of Minnesota Minneapolis, MN, 55455 EMAIL |
| 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. |