Optimizing Multiple Simultaneous Objectives for Voting and Facility Location
Authors: Yue Han, Christopher Jerrett, Elliot Anshelevich
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present tight bounds on how well any pair of such objectives (e.g., max and sum) can be simultaneously approximated compared to their optimum outcomes. In particular, we show that for any such pair of objectives, it is always possible to choose an outcome which simultaneously approximates both objectives within a factor of 1 + 2. In this paper we instead attempt to simultaneously minimize multiple objectives. |
| Researcher Affiliation | Academia | Rensselaer Polytechnic Institute, 110 Eighth Street, Troy, New York 12180 hany4@rpi.edu, jerrec@rpi.edu, eanshel@cs.rpi.edu |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | No | The paper focuses on theoretical analysis and does not mention using any datasets for training or empirical evaluation, thus no information about public availability of such datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not describe any experimental validation process with dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup that would require hardware, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any implementation details that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on mathematical proofs and approximation bounds, not empirical experiments. Therefore, it does not provide details about an experimental setup, hyperparameters, or training configurations. |