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..
Pizza Sharing Is PPA-Hard
Authors: Argyrios Deligkas, John Fearnley, Themistoklis Melissourgos4957-4965
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the computational complexity of computing solutions for the straight-cut and square-cut pizza sharing problems. We show that finding an approximate solution is PPAhard for the straight-cut problem, and PPA-complete for the square-cut problem, while finding an exact solution for the square-cut problem is FIXP-hard and in BU. Our PPAhardness results apply even when all mass distributions are unions of non-overlapping squares, and our FIXP-hardness result applies even when all mass distributions are unions of weighted squares and right-angled triangles. We also prove that decision variants of the square-cut problem are hard: the approximate problem is NP-complete, and the exact problem is ETR-complete. |
| Researcher Affiliation | Academia | Royal Holloway University of London, UK, University of Liverpool, UK, University of Essex, UK |
| Pseudocode | No | The paper focuses on theoretical proofs and complexity results and does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statements about releasing source code or links to a code repository. This is a theoretical paper, and thus, code release is not typically part of its scope. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments with datasets. It defines 'mass distributions' conceptually for its theoretical analysis but does not use actual training data. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not report on any empirical experiments that would require specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not report on any empirical experiments that would require detailing software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any empirical experiments or their setup, such as hyperparameters or training configurations. |