Computing an Approximately Optimal Agreeable Set of Items
Authors: Pasin Manurangsi, Warut Suksompong
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We consider three well-known models for representing the preferences of the agents: ordinal preferences on single items, the value oracle model, and additive utilities. In each of these models, we establish virtually tight bounds on the approximation ratio that can be obtained by algorithms running in polynomial time. |
| Researcher Affiliation | Academia | Pasin Manurangsi UC Berkeley pasin@berkeley.edu Warut Suksompong Stanford University warut@cs.stanford.edu |
| Pseudocode | No | The paper describes algorithms verbally and through mathematical proofs (e.g., in Theorem 3 for the approximation algorithm), but does not present a formally labeled pseudocode or algorithm block. |
| Open Source Code | No | The paper does not include a statement about releasing source code or a link to a code repository. It mentions a full version of the paper for proof details, but not for code: "The details of the proof can be found in the full version of this paper [Manurangsi and Suksompong, 2017]." |
| Open Datasets | No | This is a theoretical paper and does not involve the use of datasets for training or evaluation. Therefore, no information on public datasets is provided. |
| Dataset Splits | No | This is a theoretical paper and does not involve the use of datasets for training or evaluation. Therefore, no information on dataset splits is provided. |
| Hardware Specification | No | This is a theoretical paper and does not describe experiments that would require specific hardware for execution. |
| Software Dependencies | No | This is a theoretical paper describing algorithms and proofs; it does not list any specific software dependencies with version numbers for experimental setup. |
| Experiment Setup | No | This is a theoretical paper and does not describe an experimental setup with hyperparameters or training settings. |