Large-Scale Quadratically Constrained Quadratic Program via Low-Discrepancy Sequences
Authors: Kinjal Basu, Ankan Saha, Shaunak Chatterjee
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | 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 {kbasu, asaha, shchatte}@linkedin.com |
| 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. |