NESTT: A Nonconvex Primal-Dual Splitting Method for Distributed and Stochastic Optimization
Authors: Davood Hajinezhad, Mingyi Hong, Tuo Zhao, Zhaoran Wang
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Numerical Results In this section we evaluate the performance of NESTT. Consider the high dimensional regression problem with noisy observation [16], where M observations are generated by y = Xν + ϵ. ... In Fig. 1 we compare different algorithms in terms of the gap 1/βf(zr) 2. ... In Table 2 we further compare different algorithms when changing the number of component functions (i.e., the number of minibatches N) while the rest of the setup is as above. |
| Researcher Affiliation | Academia | Department of Industrial & Manufacturing Systems Engineering and Department of Electrical & Computer Engineering, Iowa State University, Ames, IA, {dhaji,mingyi}@iastate.edu School of Industrial and Systems Engineering, Georgia Institute of Technology tourzhao@gatech.edu Department of Operations Research, Princeton University,zhaoran@princeton.edu |
| Pseudocode | Yes | Algorithm 1 NESTT-G Algorithm |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that open-source code for the described methodology is available. |
| Open Datasets | No | To test the performance of the proposed algorithm, we generate the problem following similar setups as [16]. Let X = (X1; , XN) RM P with P i Ni = M and each Xi RNi P corresponds to Ni data points, and it is generated from i.i.d Gaussian. This indicates the data was generated, not taken from a publicly accessible source with provided access details. |
| Dataset Splits | No | The paper does not provide specific dataset split information (percentages, sample counts, or citations to predefined splits) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | We set M = 100,000, P = 5000, N = 50, K = 22 P,and R = ν 1. We implement NESTT-G/E, the SGD, and the nonconvex SAGA proposed in [21] with stepsize β = 1 3Lmax N2/3 (with Lmax := maxi Li). |