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..
On Sampling Complexity of the Semidefinite Affine Rank Feasibility Problem
Authors: Igor Molybog, Javad Lavaei1568-1575
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This is followed by a heuristic algorithm based on semidefinite relaxation and an experimental proof of its performance on a large sample of synthetic data. Numerical results In this section, we present and study a randomized algorithm for solving the SARF problem via an SDP relaxation that is based on the theoretical results of this paper. Algorithm 1 iteratively solves SDP relaxations of the problem with randomly sampled objective matrices. Under the assumption that no prior information is available about the unknown solution, we sample N uniformly since it belongs to the compact set T + n;k that is isomorphic to the Grassmann manifold Gn;k. We present experimental results on the performance of Algorithm 1 on a large set of synthetic data. |
| Researcher Affiliation | Academia | Igor Molybog,1 Javad Lavaei1 1University of California at Berkeley EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Heuristic algorithm for solving the Semidefinite Affine Rank Feasibility problem (1) |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper uses 'synthetic data' generated by the authors following a described procedure, rather than a pre-existing, publicly available dataset. |
| Dataset Splits | No | The paper discusses synthetic data generation and experimental setup but does not specify explicit training, validation, or test dataset splits with percentages or sample counts. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper states: 'The experiments have been scripted in Python with the use of CVXOPT as the mathematical optimization library.' However, it does not provide version numbers for Python or CVXOPT. |
| Experiment Setup | Yes | The 'Implementation' section details how the data for the experiments is generated (e.g., random sampling of Mr and X, Haar distributions, specific indices), how the objective matrix N is sampled, and how parameters like n, k, and m are used in the generation process. This constitutes a specific experimental setup description. |