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..
Minimum Weight Perfect Matching via Blossom Belief Propagation
Authors: Sung-Soo Ahn, Sejun Park, Michael Chertkov, Jinwoo Shin
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we develop the first such algorithm, coined Blossom-BP, for solving the minimum weight matching problem over arbitrary graphs. Each step of the sequential algorithm requires applying BP over a modified graph constructed by contractions and expansions of blossoms, i.e., odd sets of vertices. Our scheme guarantees termination in O(n2) of BP runs, where n is the number of vertices in the original graph. In essence, the Blossom-BP offers a distributed version of the celebrated Edmonds Blossom algorithm by jumping at once over many sub-steps with a single BP. Moreover, our result provides an interpretation of the Edmonds algorithm as a sequence of LPs. |
| Researcher Affiliation | Collaboration | School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea Theoretical Division and Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, USA |
| Pseudocode | Yes | Blossom-LP algorithm A. Solving LP on a contracted graph. ... B. Updating parameters. ... C. Termination. ... |
| Open Source Code | No | The paper does not provide any concrete access to source code for the described methodology. |
| Open Datasets | No | This is a theoretical paper and does not mention the use of any specific datasets for training. |
| Dataset Splits | No | This is a theoretical paper and does not discuss dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not provide details about an experimental setup, hyperparameters, or training configurations. |