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..
A consistently adaptive trust-region method
Authors: Fadi Hamad, Oliver Hinder
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our algorithm on learning linear dynamical systems [48], matrix completion [49], and the CUTEst test set [50]. Appendix D contains the complete set of results from our experiments. |
| Researcher Affiliation | Academia | Fadi Hamad Department of Industrial Engineering University of Pittsburgh Pittsburgh, PA 15261 EMAIL Oliver Hinder Department of Industrial Engineering University of Pittsburgh Pittsburgh, PA 15261 EMAIL |
| Pseudocode | Yes | Algorithm 1: Consistently Adaptive Trust Region Method (CAT) |
| Open Source Code | Yes | Our method is implemented in an open-source Julia module available at https://github.com/ fadihamad94/CAT-Neur IPS. |
| Open Datasets | Yes | We test our algorithm on learning linear dynamical systems [48], matrix completion [49], and the CUTEst test set [50]. For our experiment, we use the public data set of Ausgrid, but we only use the data from a single substation. Details are provided in Appendix D.2. |
| Dataset Splits | No | The paper does not explicitly specify dataset splits (e.g., percentages or counts for training, validation, and test sets). |
| Hardware Specification | Yes | We perform our experiments using Julia 1.6 on a Linux virtual machine that has 8 CPUs and 16 GB RAM. |
| Software Dependencies | No | We perform our experiments using Julia 1.6 on a Linux virtual machine that has 8 CPUs and 16 GB RAM. (Only Julia version is given; specific versions for libraries like Optim.jl or CUTEst.jl are not provided.) |
| Experiment Setup | Yes | For these experiments, the selection of the parameters (unless otherwise specified) is as follow: r1 = 1.0, β = 0.1, θ = 0.1, ω = 8.0, γ1 = 0.0, γ2 = 0.8, and γ3 = 1.0. When implementing Algorithm 1 with some target tolerance ϵ, we immediately terminate when we observe a point xk with f(xk + dk) ϵ. This also includes the case when we check the inner termination criteria for the trust-region subproblem. The full details of the implementation are described in Appendix C. Our algorithm is stopped as soon f(xk + dk) is smaller than 10 5... We used 10000 as an iteration limit and any run exceeding this is considered a failure. |