Search Strategies for Topological Network Optimization
Authors: Michael D. Moffitt10299-10308
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we evaluate our approach on a new parameterized benchmark suite, demonstrating over an order of magnitude improvement in performance as compared to a baseline implementation. |
| Researcher Affiliation | Industry | Michael D. Moffitt Google moffitt@google.com |
| Pseudocode | Yes | Algorithm 1: NETWORK-OPT |
| Open Source Code | Yes | Source code (including visualization utilities) is available at https://github.com/google/network-opt |
| Open Datasets | No | The paper mentions a parameterized benchmark suite based on the E12 series of preferred values, which is a standard, but it does not provide a direct link, DOI, or specific repository for a dataset file. It describes how the values for S and fT are assigned based on this standard. |
| Dataset Splits | No | The paper does not explicitly mention specific train/validation/test splits, sample counts for splits, or cross-validation setup. It evaluates performance on parameterized problems of increasing 'n'. |
| Hardware Specification | Yes | All experiments were conducted on a Debian Linux workstation powered by a 2.20GHz Intel Xeon CPU and 32gb of RAM. |
| Software Dependencies | No | The paper mentions the operating system "Debian Linux" but does not specify versions for any other software dependencies such as programming languages, libraries, or frameworks. |
| Experiment Setup | Yes | A timeout of one week was imposed on all solvers. For any experiment using tabulation, the hyperparameter M was set to n/2 . |