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 [1].
Augmentative Message Passing for Traveling Salesman Problem and Graph Partitioning
Authors: Siamak Ravanbakhsh, Reihaneh Rabbany, Russell Greiner
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments. Here we evaluate our method over five benchmark datasets: (I) TSPLIB, which contains a variety of real-world benchmark instances... The results in Figure 2(2nd column from left) reports the optimality ratio i.e., ratio of the tour found by message passing, to the optimal tour. |
| Researcher Affiliation | Academia | Siamak Ravanbakhsh Department of Computing Science University of Alberta Edmonton, AB T6G 2E8 EMAIL Reihaneh Rabbany Department of Computing Science University of Alberta Edmonton, AB T6G 2E8 EMAIL Russell Greiner Department of Computing Science University of Alberta Edmonton, AB T6G 2E8 EMAIL |
| Pseudocode | No | The paper describes mathematical message update equations but does not provide pseudocode or a clearly labeled algorithm block with structured steps. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | Here we evaluate our method over five benchmark datasets: (I) TSPLIB, which contains a variety of real-world benchmark instances... For graph partitioning, we experimented with a set of classic benchmarks Obtained form Mark Newman s website: http://www-personal.umich.edu/ mejn/ netdata/ |
| Dataset Splits | No | The paper describes the datasets used (TSPLIB, Mark Newman's benchmarks) but does not specify explicit training, validation, and test dataset splits or percentages for reproducing the experiments. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions software like 'Concorde' and 'CPLEX' but does not specify their version numbers or other ancillary software dependencies with versions. |
| Experiment Setup | Yes | All experiments use Tmax = 200 iterations, ϵmax = median{d(e)}e E and damping with λ = .2. We used decimation, and fixed 10% of the remaining variables (out of N) per iteration of decimation. For message passing, we use λ = .1, ϵmax = median{|ω(e) ωnull(e)|}e E Enull and Tmax = 10. |