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..
A primal-dual method for conic constrained distributed optimization problems
Authors: Necdet Serhat Aybat, Erfan Yazdandoost Hamedani
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We tested DPDA-S and DPDA-D on a primal linear SVM problem where the data is distributed among the computing nodes in N. Relative suboptimality and relative consensus violation, i.e., max(i,j) E [w i bi] [w j bj] / [w b ] , and absolute feasibility violation are plotted against iteration counter in Fig. 3, where [w b ] denotes the optimal solution to the central problem. |
| Researcher Affiliation | Academia | Necdet Serhat Aybat Department of Industrial Engineering Penn State University University Park, PA 16802 EMAIL Erfan Yazdandoost Hamedani Department of Industrial Engineering Penn State University University Park, PA 16802 EMAIL |
| Pseudocode | Yes | Figure 1: Distributed Primal Dual Algorithm for Static G (DPDA-S) and Figure 2: Distributed Primal Dual Algorithm for Dynamic Gt (DPDA-D) |
| Open Source Code | No | The paper mentions implementing the proposed algorithms and presents numerical results, but it does not provide any specific links or explicit statements about the public availability of its source code. |
| Open Datasets | No | The dataset used is synthetically generated: '{xℓ}ℓ S is generated from two-dimensional multivariate Gaussian distribution with covariance matrix Σ = [1, 0; 0, 2] and with mean vector either m1 = [ 1, 1]T or m2 = [1, 1]T with equal probability.' No link or citation to a publicly available dataset is provided. |
| Dataset Splits | No | The paper specifies the partition of data into 'Stest' and 'Strain' with sizes '|Stest| = 600' and '|Strain| = 300'. However, it does not explicitly mention or provide details for a separate validation dataset split. |
| Hardware Specification | No | The paper describes experimental setups but does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency details, such as library names with version numbers, required to replicate the experiments. |
| Experiment Setup | Yes | The experiment was performed for C = 2, |N| = 10, s = 900 such that |Stest| = 600, |Si| = 30 for i N, i.e., |Strain| = 300, and qk = k. |