A consistently adaptive trust-region method
Authors: Fadi Hamad, Oliver Hinder
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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 fah33@pitt.edu Oliver Hinder Department of Industrial Engineering University of Pittsburgh Pittsburgh, PA 15261 ohinder@pitt.edu |
| 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. |