On the Complexity of Finite-Sum Smooth Optimization under the Polyak–Łojasiewicz Condition
Authors: Yunyan Bai, Yuxing Liu, Luo Luo
ICML 2024 | Conference PDF | Archive PDF | Plain Text | 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. |