Fair Knapsack

Authors: Till Fluschnik, Piotr Skowron, Mervin Triphaus, Kai Wilker1941-1948

AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical Our complexity results are outlined in Table 1. In summary, we showed that computing an individually best or a diverse knapsack can be done efficiently under some constraints. On the contrary, we give multiple evidences that computing a fair knapsack is computationally hard.
Researcher Affiliation Academia 1Algorithmics and Computational Complexity, Faculty IV, TU Berlin, Berlin, Germany till.fluschnik@tu-berlin.de, {mervin.triphaus,wilker}@campus.tu-berlin.de 2Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Warsaw, Poland p.skowron@mimuw.edu.pl
Pseudocode No The paper does not contain structured pseudocode or algorithm blocks.
Open Source Code No The paper does not provide concrete access to source code for the described methodology.
Open Datasets No The paper is theoretical and does not discuss datasets or their public availability.
Dataset Splits No The paper is theoretical and does not describe dataset splits for training, validation, or testing.
Hardware Specification No The paper is theoretical and does not mention any hardware specifications used for experiments.
Software Dependencies No The paper is theoretical and does not mention any specific software dependencies with version numbers.
Experiment Setup No The paper is theoretical and does not describe any experimental setup details or hyperparameters.