Simplification and Improvement of MMS Approximation
Authors: Hannaneh Akrami, Jugal Garg, Eklavya Sharma, Setareh Taki
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we significantly simplify the analysis of this algorithm and also improve the existence guarantee to a factor of (3/4 + 1/36, 3/4 + 1/(4n-1))). Furthermore, we present a tight example of this algorithm, showing that this may be the best factor one can hope for with the current techniques. |
| Researcher Affiliation | Collaboration | Hannaneh Akrami1,2 , Jugal Garg3 , Eklavya Sharma3 and Setareh Taki4 1Max Planck Institute for Informatics, Germany 2Graduiertenschule Informatik, Universit at des Saarlandes, Germany 3University of Illinois at Urbana-Champaign, USA 4Grubhub, USA |
| Pseudocode | Yes | Algorithm 1 normalize((N, M, v)), Algorithm 2 approx MMS(I, α), Algorithm 3 bag Fill(I, α) |
| Open Source Code | No | The paper does not provide any explicit statements or links about releasing source code for the methodology described. |
| Open Datasets | No | This is a theoretical paper focused on algorithm analysis and approximation factors; it does not utilize datasets in an empirical context. Therefore, no information about public dataset availability is provided. |
| Dataset Splits | No | This is a theoretical paper that does not involve empirical experiments with dataset splits. No specific information about training, validation, or test splits is provided. |
| Hardware Specification | No | This is a theoretical paper focused on algorithm analysis and mathematical proofs, not empirical experiments. Therefore, no hardware specifications for running experiments are provided. |
| Software Dependencies | No | This is a theoretical paper focused on algorithm analysis and mathematical proofs, not empirical experiments. Therefore, no specific software dependencies with version numbers are provided. |
| Experiment Setup | No | This is a theoretical paper focused on algorithm analysis and mathematical proofs, not empirical experiments. No specific experimental setup details such as hyperparameters or training configurations are provided. |