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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
First-Order Methods for Linearly Constrained Bilevel Optimization
Authors: Guy Kornowski, Swati Padmanabhan, Kai Wang, Zhe Zhang, Suvrit Sra
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our numerical experiments verify these guarantees. |
| Researcher Affiliation | Academia | Weizmann Institute of Science. EMAIL Massachusetts Institute of Technology. EMAIL Georgia Institute of Technology. EMAIL Purdue University. EMAIL Massachusetts Institute of Technology. EMAIL |
| Pseudocode | Yes | Algorithm 1 Inexact Gradient Oracle for Bilevel Program with Linear Equality Constraint |
| Open Source Code | Yes | The implementation can be found in https://github.com/guaguakai/constrained-bilevel-optimization. |
| Open Datasets | No | We generate instances of the following constrained bilevel optimization problem: minx c y + 0.01 x 2 + 0.01 y 2 s.t. y = arg miny:h(x,y) 0 1 2y Qy + x Py, (6.1) where h(x, y) = Ay b is a dh-dim linear constraint. The PSD matrix Q Rdy dy, c Rdy, P Rdx dy, and constraints A Rdh dy, b Rdh are randomly generated from normal distributions (cf. Appendix K). |
| Dataset Splits | No | The paper describes generating synthetic data instances but does not specify any training, validation, or test dataset splits, percentages, or absolute sample counts. |
| Hardware Specification | Yes | All experiments were run on a computing cluster with Dual Intel Xeon Gold 6226 CPUs @ 2.7 GHz and DDR4-2933 MHz DRAM. No GPU was used, and we used 1 core with 8GB RAM per instance of the experiment. |
| Software Dependencies | No | All algorithms are implemented in Py Torch [94] to compute gradients, and using Cvxpy [95] to solve the LL problem and the penalty minimization problem. (No version numbers provided for PyTorch or CVXPY) |
| Experiment Setup | Yes | Both algorithms use Adam [90] to control the learning rate, and are averaged over 10 random seeds. We run Algorithm 3 using Algorithm 4 on the bilevel optimization in the toy example in Problem L.1 with dx = 100, dy = 200, nconst = dy/5, and accuracy α = 0.1. The cutoff time for running the algorithms is set to be 6 hours. |