Multiwinner Elections under Minimax Chamberlin-Courant Rule in Euclidean Space

Authors: Chinmay Sonar, Subhash Suri, Jie Xue

IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical We show that the problem is NPhard in any dimension d 2, and also provably hard to approximate. Our main results are three polynomial-time approximation schemes, each of which finds a committee with provably good minimax score.
Researcher Affiliation Academia 1University of California, Santa Barbara 2New York University, Shanghai {csonar, suri}@cs.ucsb.edu, jiexue@nyu.edu
Pseudocode No The paper describes algorithms verbally (e.g., "Our algorithm begins with an empty committee T = and iteratively adds new candidates to T using the following three steps until S[T] = V ") but does not present them in a structured pseudocode block.
Open Source Code No The paper does not provide any statements about open-sourcing code or links to repositories.
Open Datasets No The paper is theoretical and focuses on computational complexity and approximation algorithms in Euclidean space. It does not use specific datasets for training or empirical evaluation.
Dataset Splits No The paper is theoretical and does not involve empirical experiments with dataset splits (training, validation, test).
Hardware Specification No The paper is theoretical and does not report on computational experiments that would require specific hardware. Therefore, no hardware specifications are mentioned.
Software Dependencies No The paper is theoretical and focuses on proofs and algorithm design. It does not describe any specific software dependencies or version numbers, as no empirical implementation or experiments are detailed.
Experiment Setup No The paper is theoretical and does not describe an experimental setup, hyperparameters, or training configurations, as it does not involve empirical experiments.