Summary of Scalable Sparse Regression For Model Discovery: the Fast Lane to Insight, by Matthew Golden
Scalable Sparse Regression for Model Discovery: The Fast Lane to Insight
by Matthew Golden
First submitted to arxiv on: 14 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Analysis, Statistics and Probability (physics.data-an); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel, model-agnostic sparse regression algorithm is proposed to learn governing equations directly from data in dynamical systems. The approach, called Scalable Pruning for Rapid Identification of Null vecTors (SPRINT), leverages iterative Singular Value Decompositions (SVD) and bisection with analytic bounds to quickly identify optimal rank-1 modifications to null vectors. This accelerated scheme maintains sensitivity to small coefficients and is computationally efficient for large symbolic libraries. The algorithm balances quantitative accuracy, qualitative simplicity, and human interpretability, making it a powerful tool for dynamical systems analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to learn rules that describe complex systems is presented in this paper. It uses math and data to find simple equations that explain how the system behaves. This approach is useful because it can handle big datasets quickly and gives insights into what’s happening in the system. The method, called SPRINT, is a new way of doing something that was previously very slow. |
Keywords
» Artificial intelligence » Pruning » Regression
