Matchings under One-Sided Preferences with Soft Quotas
Authors: Santhini K. A., Raghu Raman Ravi, Meghana Nasre
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present efficient algorithms based on flow-networks to solve these optimization problems. Theorem 1. OPT-SIGN-MIN-MAX and OPT-SIGN-MIN-TOT admit polynomial time algorithms, where OPT can be one of rank-maximality (RMM) or fairness (FAIR). Theorem 2. OPT-MIN-MAX and OPT-MIN-TOT admit polynomial time algorithms, where OPT can be one of rank-maximality or fairness. |
| Researcher Affiliation | Academia | Santhini K. A.1 , Raghu Raman Ravi2 and Meghana Nasre1 1Indian Institute of Technology Madras 2ETH Zurich {santhini, meghana}@cse.iitm.ac.in, raghu.ravi.raman@gmail.com |
| Pseudocode | No | The paper describes algorithms textually and uses flow network diagrams, but it does not contain a clearly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is open-source or publicly available. |
| Open Datasets | No | The paper is theoretical and does not use or reference any datasets for training or evaluation, therefore no information about publicly available datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with datasets, thus no information about training, validation, or test splits is provided. |
| Hardware Specification | No | The paper describes theoretical algorithms and does not report on empirical experiments, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper describes theoretical algorithms and does not report on empirical experiments, therefore no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and describes algorithm design and proofs, not empirical experiments. Therefore, no experimental setup details like hyperparameters or training configurations are provided. |