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..
Functionally Constrained Algorithm Solves Convex Simple Bilevel Problem
Authors: Huaqing Zhang, Lesi Chen, Jing Xu, Jingzhao Zhang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate our proposed methods on two different bilevel problems with smooth objectives. We compare the performance of FC-Bi Osm with existing methods, including a-IRG [14], Bi-SG [19], CG-Bi O[13], AGM-Bi O [6], PB-APG[10], and Bisec-Bi O[28]. |
| Researcher Affiliation | Collaboration | 1IIIS, Tsinghua University 2Shanghai Qizhi Institute 3Shanghai AI Lab |
| Pseudocode | Yes | Algorithm 1 Functionally Constrained Bilevel Optimizer (FC-Bi O) |
| Open Source Code | No | No explicit statement about the authors' own code being open-source is present in the provided text. The text only mentions that implementations of other methods (CG-Bi O and a-IRG) are based on code available online. |
| Open Datasets | Yes | We use the Wikipedia Math Essential dataset [22]... using the rcv1.binary dataset from LIBSVM [8, 16] |
| Dataset Splits | Yes | We uniformly sample m = 5000 instances as the training dataset (Atr, btr), and m instances as the validation dataset (Aval, bval). |
| Hardware Specification | Yes | All experiments are implemented using MATLAB R2022b on a PC running Windows 11 with a 12th Gen Intel(R) Core(TM) i7-12700H CPU (2.30 GHz) and 16GB RAM. |
| Software Dependencies | Yes | All experiments are implemented using MATLAB R2022b |
| Experiment Setup | Yes | We set ϵf = ϵg = 10 6. For our Algorithm 1, we take a slightly different implementation, that instead of setting the maximum number of iterations of the inner subroutine to be T = T/N, we preset T = 8000. If current xk already satisfies ψ(t, xk) ϵ/2, then terminate the inner subroutine directly. We adopt the warm-start strategy as described in Appendix B.2. We set L = 0 since f(x) is nonnegative. For FC-Bi OLip, we set η = 3 10 4. |