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 [1].
Randomized Strategies for Robust Combinatorial Optimization
Authors: Yasushi Kawase, Hanna Sumita7876-7883
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | To solve the problem, we propose two types of schemes for designing approximation algorithms. One scheme is for the case when objective functions are linear. It ๏ฌrst ๏ฌnds an approximately optimal aggregated strategy and then retrieves a desired solution with little loss of the objective value. The approximation ratio depends on a relaxation of an independence system polytope. |
| Researcher Affiliation | Academia | Yasushi Kawase Tokyo Institute of Technology RIKEN AIP Center EMAIL Hanna Sumita Tokyo Metropolitan University EMAIL |
| Pseudocode | Yes | Algorithm 1: MWU for the robust optimization |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that its source code is open or publicly available. |
| Open Datasets | No | The paper describes theoretical problems and algorithms (e.g., knapsack constraint, matroid constraint) but does not refer to specific real-world datasets used for training. |
| Dataset Splits | No | The paper does not discuss empirical validation with dataset splits such as training, validation, or test sets. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used, as its focus is on theoretical algorithms and their approximation ratios. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers, which is typical for theoretical work focused on algorithmic design. |
| Experiment Setup | No | The paper does not describe specific experimental setup details such as hyperparameters or system-level training settings, as it is a theoretical paper proposing algorithms and proving their properties. |