Min-Max Problems on Factor Graphs
Authors: Siamak Ravanbakhsh, Christopher Srinivasa, Brendan Frey, Russell Greiner
ICML 2014 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results suggest that message passing often provides near optimal min-max solutions for moderate size instances. |
| Researcher Affiliation | Academia | Siamak Ravanbakhsh EMAIL Christopher Srinivasa EMAIL Brendan Frey EMAIL Russell Greiner EMAIL Computing Science Dept., University of Alberta, Edmonton, AB T6G 2E8 Canada PSI Group, University of Toronto, ON M5S 3G4 Canada |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks that are clearly labeled as 'Algorithm' or 'Pseudocode'. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described, such as a specific repository link, an explicit code release statement, or code in supplementary materials. |
| Open Datasets | Yes | Table 2. Some optimal binary codes from Litsyn et al. 1999 recovered by K-packing factor-graph in the order of increasing y. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning into train/validation/test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, memory amounts, or detailed computer specifications) used for running its experiments. It only mentions general experimental settings. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers (e.g., 'Python 3.8', 'CPLEX 12.4'), that are needed to replicate the experiment. |
| Experiment Setup | Yes | The number of iterations T is the only parameter of PBP and increasing T, increases the chance of finding a solution (Only downside is time complexity; nb., no chance of a false positive). PBP starts at γ = 0 and linearly increases γ at each iteration, ending at γ = 1 at its final iteration. (T = 50 iterations for PBP (Figure 3), T = 500 for PBP (Figure 5), T = 1000 iterations (Table 2), T = 5000 for PBP (Figure 5)). |