Sample-Efficient Geometry Reconstruction from Euclidean Distances using Non-Convex Optimization
Authors: Ipsita Ghosh, Abiy Tasissa, Christian Kümmerle
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Furthermore, we assess data efficiency, scalability and generalizability of different reconstruction algorithms through numerical experiments with simulated data as well as real-world data, demonstrating the proposed algorithm s ability to identify the underlying geometry from fewer distance samples compared to the state-of-the-art. |
| Researcher Affiliation | Academia | Ipsita Ghosh Department of Computer Science University of North Carolina at Charlotte ighosh2@charlotte.edu Abiy Tasissa Department of Mathematics Tufts University Abiy.Tasissa@tufts.edu Christian Kümmerle Department of Computer Science University of North Carolina at Charlotte kuemmerle@charlotte.edu |
| Pseudocode | Yes | Algorithm 1 Matrix IRLS for Euclidean Distance Geometry Problems |
| Open Source Code | Yes | The Matlab code can be found at github_EDG-IRLS |
| Open Datasets | Yes | For our experiments, we determine the structures of a protein molecule (1BPM) from the Protein Data Bank [HMBFGG+00] |
| Dataset Splits | No | No explicit mention of training, validation, or test dataset splits (e.g., percentages or sample counts) was found. The paper describes random sampling of distance pairs for input, not traditional dataset splits for train/validation/test. |
| Hardware Specification | Yes | The experiements were performed on a single node of a compute cluster equipped with dual 24-core Intel Xeon Gold 6248R CPUs, utilizing 32 parallel tasks. |
| Software Dependencies | No | The paper mentions using 'Matlab code' and adapts implementations for 'Scaled SGD', 'ALM', and 'Rie EDG', but does not provide specific version numbers for these software dependencies or libraries. |
| Experiment Setup | Yes | The input parameter for Matrix IRLS includes the number of outer IRLS iterations N 0, the number of inner iterations N 0 inner, the tolerance, which is the stopping criteria for the algorithm tol0 inner. For large datasets like the UScities data and Protein data N = 400, although the algorithm converges within 120 iterations. We run the experiments with N 0 inner = 2000 and tolinner = 10 10. In a smaller setup of the synthetic data we perform the experiments with n = 500 points with same parameters. although convergence of Matrix IRLS is observed much faster. To put more emphasis on the per iteration error, we study the experiment on per-iterate analysis with more iterations. |