Maxmin Participatory Budgeting
Authors: Gogulapati Sreedurga, Mayank Ratan Bhardwaj, Yadati Narahari
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the first part, we prove that MPB is computationally hard and give pseudo-polynomial time and polynomial-time algorithms when parameterized by certain well-motivated parameters. We propose an algorithm that achieves for MPB, additive approximation guarantees for restricted spaces of instances and empirically show that our algorithm in fact gives exact optimal solutions on real-world PB datasets. |
| Researcher Affiliation | Academia | Gogulapati Sreedurga , Mayank Ratan Bhardwaj and Yadati Narahari Indian Institute of Science {gogulapatis, mayankb, narahari}@iisc.ac.in |
| Pseudocode | No | The paper describes algorithms (e.g., dynamic programming, LP-rounding) and formulates an integer linear program, but it does not include a distinct, labeled block of pseudocode or an algorithm for any of them. |
| Open Source Code | No | The paper states: "Information on some such datasets is included at https://github.com/Participatory-Budgeting/maxmin 2022." and "The datasets and corresponding results are included at https://github.com/Participatory-Budgeting/maxmin 2022." This link points to datasets and results, not the source code for the methodology or algorithms presented in the paper. |
| Open Datasets | Yes | Empirically it is found to exhibit remarkable performance to give exact optimal solutions on all the PB datasets available in [Stolicki et al., 2020]. The datasets and corresponding results are included at https://github.com/Participatory-Budgeting/maxmin 2022. |
| Dataset Splits | No | The paper mentions using "real-world PB datasets" but does not specify any train, validation, or test splits, nor does it refer to standard predefined splits for the datasets used. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments (e.g., GPU models, CPU types, or memory specifications). |
| Software Dependencies | No | The paper does not provide specific version numbers for any software components, libraries, or solvers used in their experiments. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameters, optimization settings, or other configuration parameters for training or running their algorithms. |