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 [1].
The non-convex Burer-Monteiro approach works on smooth semidefinite programs
Authors: Nicolas Boumal, Vlad Voroninski, Afonso Bandeira
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Interestingly, known algorithms for optimization on manifolds converge to second-order critical points,2 regardless of initialization [Boumal et al., 2016]. ... Indeed, the numerical experiments clearly show that high accuracy solutions can be computed fast using optimization on manifolds, at least for certain applications. |
| Researcher Affiliation | Academia | Nicolas Boumal Department of Mathematics Princeton University EMAIL Vladislav Voroninski Department of Mathematics Massachusetts Institute of Technology EMAIL Afonso S. Bandeira Department of Mathematics and Center for Data Science Courant Institute of Mathematical Sciences, New York University EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions "Manopt, a Matlab toolbox for optimization on manifolds. Journal of Machine Learning Research, 15:1455 1459, 2014. URL http://www.manopt.org." as a tool, but it does not state that the specific code for the methodology described in *this* paper is released or available. |
| Open Datasets | No | The paper mentions problem types like Max-Cut and community detection, and in Appendix B, a "cycle graph of n=20 nodes" is used for a numerical demonstration. However, it does not provide concrete access information (link, DOI, specific citation with authors/year, or repository) for a public dataset used in experiments. |
| Dataset Splits | No | The paper does not specify dataset splits (e.g., training, validation, test percentages or counts) needed to reproduce data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions "Manopt [Boumal et al., 2014]", "CVX: Matlab software for disciplined convex programming", and "SDPT3 a MATLAB software package for semidefinite programming", but does not provide specific version numbers for these software dependencies (e.g., "Manopt vX.Y", "CVX 2.1", "SDPT3 4.0"). |
| Experiment Setup | Yes | We use the Riemannian trust-region method as implemented in Manopt [Boumal et al., 2014], with default parameters for the trust-region subproblem, and stopping criterion gradnorm < 10^-8. |