Augmentative Message Passing for Traveling Salesman Problem and Graph Partitioning

Authors: Siamak Ravanbakhsh, Reihaneh Rabbany, Russell Greiner

NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | 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 mravanba@ualberta.ca Reihaneh Rabbany Department of Computing Science University of Alberta Edmonton, AB T6G 2E8 rabbanyk@ualberta.ca Russell Greiner Department of Computing Science University of Alberta Edmonton, AB T6G 2E8 rgreiner@ualberta.ca
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.