A Primal-Dual-Assisted Penalty Approach to Bilevel Optimization with Coupled Constraints
Authors: Liuyuan Jiang, Quan Xiao, Victor Tenorio, Fernando Real-Rojas, Antonio G. Marques, Tianyi Chen
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We establish rigorous convergence theory for the proposed algorithm and demonstrate its effectiveness on two well-known real-world applications hyperparameter selection in support vector machine (SVM) and infrastructure planning in transportation networks using the real data from the city of Seville. |
| Researcher Affiliation | Academia | Rensselaer Polytechnic Institute, Troy, NY, United States King Juan Carlos University, Madrid, Spain |
| Pseudocode | Yes | Algorithm 1 Meta algorithm: BLOCC |
| Open Source Code | Yes | The code is available at https://github.com/Liuyuan999/Penalty Based Lagrangian Bilevel. |
| Open Datasets | Yes | We test the performance of our algorithm BLOCC with γ = 12 when training a linear SVM model on the diabetes [20] and fourclass datasets [29]. |
| Dataset Splits | Yes | The experiments are executed for 50 different random train-validation-test splits, with the bold line representing the mean, and the shaded regions being the standard deviation. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, memory, or specific computing cluster configurations used for running the experiments. |
| Software Dependencies | No | The paper mentions software by name (e.g., BLOCC, LV-HBA, GAM) and sets hyperparameters for them, but does not specify version numbers for any software components, libraries, or programming languages. |
| Experiment Setup | Yes | We use γ = 12 and η = 0.01 for running our BLOCC and we apply α = 0.01, γ1 = 0.1, γ2 = 0.1, η = 0.001 for LV-HBA and α = 0.05, ϵ = 0.005 for GAM respectively. |