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..
Large-Scale Quadratically Constrained Quadratic Program via Low-Discrepancy Sequences
Authors: Kinjal Basu, Ankan Saha, Shaunak Chatterjee
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results are also shown to prove scalability as well as improved quality of approximation in practice. |
| Researcher Affiliation | Industry | Kinjal Basu, Ankan Saha, Shaunak Chatterjee Linked In Corporation Mountain View, CA 94043 EMAIL |
| Pseudocode | Yes | Algorithm 1 Point Simulation on S |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper states 'We randomly sample A, B, x0 and b keeping the problem convex.' indicating synthetic or generated data without providing access to a specific public dataset or its generation code. |
| Dataset Splits | No | The paper does not provide specific details on training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Operator Splitting or ADMM [10, 26]' and 'cvx in MATLAB using via Se Du Mi and SDPT3' but does not provide specific version numbers for any of these software components. |
| Experiment Setup | Yes | The stopping criteria throughout our simulation is same as that of Operator Splitting algorithm as presented in [26]. Throughout our simulations, we have chosen η = 2 and the number of optimal points as N = max(1024, 2m), where m is the smallest integer such that 2m ≥ 10n. |