Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Tight Approximation for Proportional Approval Voting
Authors: Szymon Dudycz, Pasin Manurangsi, Jan Marcinkowski, Krzysztof Sornat
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main result (Theorem 1) is a tight αw-approximation algorithm for a natural class of geometrically dominant w-THIELE rules, where αw is a sequence-dependent constant defined in (1). Resulting approximation ratios for selected w-THIELE rules are presented in Table 1. The algorithm is relatively simple: first we solve a linear program and then we round a solution by employing a framework called pipage rounding due to Ageev and Sviridenko (2004) and Calinescu et al. (2011). We provide a matching lower bound via a reduction from the Label Cover problem. Moreover, assuming a conjecture called Gap-ETH, we show that better approximation ratio cannot be obtained even in time f(k)*pow(n,o(k)). |
| Researcher Affiliation | Collaboration | Szymon Dudycz1 , Pasin Manurangsi2 , Jan Marcinkowski1 and Krzysztof Sornat1,3 1University of Wrocław, Poland 2Google Research, US 3Ben-Gurion University, Israel EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes the algorithm in prose, stating: "Our algorithm is based on an LP rounding technique, which requires us to first define scr E w also on a fractional solution x [0, 1]C specifying for each candidate fractionally, how much the candidate is selected." It does not include a structured pseudocode block or an algorithm box. |
| Open Source Code | No | The paper does not contain any statement about making its source code available, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper is theoretical, focusing on algorithm design, proofs, and hardness results. It does not involve empirical evaluation on datasets, and thus no public dataset information is provided. |
| Dataset Splits | No | The paper is theoretical and does not perform empirical evaluations. Therefore, it does not discuss validation splits for datasets. |
| Hardware Specification | No | The paper is theoretical and focuses on algorithm design and complexity analysis. It does not mention any hardware specifications used for computation or experimentation. |
| Software Dependencies | No | The paper is theoretical and focuses on algorithm design and complexity analysis. It does not mention any specific software dependencies or their version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any empirical experimental setup, hyperparameters, or training settings for a model. |