Summary of Black-box Approximation and Optimization with Hierarchical Tucker Decomposition, by Gleb Ryzhakov et al.
Black-Box Approximation and Optimization with Hierarchical Tucker Decomposition
by Gleb Ryzhakov, Andrei Chertkov, Artem Basharin, Ivan Oseledets
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
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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 In this paper, researchers introduce HTBB, a novel approach for multidimensional black-box approximation and gradient-free optimization. The method relies on low-rank hierarchical Tucker decomposition and MaxVol indices selection procedure. The authors test HTBB on 14 complex problems, demonstrating its robustness up to dimensions of 1000. Comparing it to classical methods and tensor train decomposition-based approaches, HTBB shows improved accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This new method helps solve big problems in many areas like science and engineering. It’s good at finding the right answers even when we don’t know how to get there. The researchers tested this method on 14 tricky problems and found it works well up to really big dimensions. This is important because it can help us make better decisions and understand complex things. |
Keywords
* Artificial intelligence * Optimization
