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..
NESTT: A Nonconvex Primal-Dual Splitting Method for Distributed and Stochastic Optimization
Authors: Davood Hajinezhad, Mingyi Hong, Tuo Zhao, Zhaoran Wang
NeurIPS 2016 | Venue PDF | 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, EMAIL School of Industrial and Systems Engineering, Georgia Institute of Technology EMAIL Department of Operations Research, Princeton University,EMAIL |
| 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). |