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..
On the Complexity of Finite-Sum Smooth Optimization under the Polyak–Łojasiewicz Condition
Authors: Yunyan Bai, Yuxing Liu, Luo Luo
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct numerical experiments to compare DRONE with centralized gradient descent (CGD) and DGD-GT, where CGD is a distributed extension of GD in client-server networks. Please see Appendix D for details. We test the algorithms on the following three problems: Hard instance, Linear regression, Logistic regression |
| Researcher Affiliation | Academia | 1School of Data Science, Fudan University, Shanghai, China 2 Shanghai Key Laboratory for Contemporary Applied Mathematics, Shanghai, China. |
| Pseudocode | Yes | Algorithm 3 DGD-GT 1: Input: initial point x0 R1 d, iteration number T, stepsize η > 0 and communication numbers K 2: X0 = 1 x0 3: S0 = F(X0) 4: for t = 0, . . . , T 1 do 5: Xt+1 = Acc Gossip(Xt ηSt, W, K) 6: St+1 = Acc Gossip(St + F(Xt+1) F(Xt), W, K) 7: end for 8: Output: uniformly sample xout from {x T (i)}n i=1 |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We evaluate the algorithms on dataset Driv Face (m = 606, d = 921, 600) (Diaz-Chito et al., 2016) for this problem. We evaluate the algorithms on dataset RCV1 (m = 20, 242, d = 47, 236) (Diaz Chito et al., 2016) for this problem. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology). |
| Hardware Specification | Yes | All of our experiments are performed on PC with Intel(R) Core(TM) i7-8550U CPU@1.80GHz processor |
| Software Dependencies | Yes | we implement the algorithms by MPI for Python 3.9. |
| Experiment Setup | Yes | For all the above problems, we set n = 32 and use a linear graph for the network of DGD-GT and DRONE, leading to that γ = (1 cos(π/32)) / (1 + cos(π/32)) 0.0024. |