Min-Max Problems on Factor Graphs
Authors: Siamak Ravanbakhsh, Christopher Srinivasa, Brendan Frey, Russell Greiner
ICML 2014 | Conference 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 MRAVANBA@UALBERTA.CA Christopher Srinivasa CHRIS@PSI.UTORONTO.CA Brendan Frey FREY@PSI.UTORONTO.CA Russell Greiner RGREINER@UALBERTA.CA 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)). |