Functionally Constrained Algorithm Solves Convex Simple Bilevel Problem
Authors: Huaqing Zhang, Lesi Chen, Jing Xu, Jingzhao Zhang
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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. |