Randomized Strategies for Robust Combinatorial Optimization
Authors: Yasushi Kawase, Hanna Sumita7876-7883
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | 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 first finds 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 kawase.y.ab@m.titech.ac.jp Hanna Sumita Tokyo Metropolitan University sumita@tmu.ac.jp |
| 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. |