Summary of Adaptive Coordinate-wise Step Sizes For Quasi-newton Methods: a Learning-to-optimize Approach, by Wei Lin et al.
Adaptive Coordinate-Wise Step Sizes for Quasi-Newton Methods: A Learning-to-Optimize Approach
by Wei Lin, Qingyu Song, Hong Xu
First submitted to arxiv on: 25 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
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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 tackle the challenge of optimizing second-order methods by introducing a novel Learning-to-Optimize (L2O) model within the Broyden-Fletcher-Goldfarb-Shanno (BFGS) framework. The L2O model leverages neural networks to predict optimal coordinate-wise step sizes, which is crucial for the stability and efficiency of optimization algorithms. The authors provide a theoretical foundation that establishes conditions for the stability and convergence of these step sizes, demonstrating significant improvements over traditional backtracking line search and hypergradient descent-based methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Optimizing second-order methods can be tricky! Researchers have been working on ways to make it more efficient and stable. One way is by using a new model called Learning-to-Optimize (L2O) within an existing framework called Broyden-Fletcher-Goldfarb-Shanno (BFGS). This L2O model uses special computer programs called neural networks to figure out the best step sizes for different parts of the problem. It’s like having a super smart helper that makes sure everything runs smoothly and quickly! The new approach is really fast, up to 7 times faster than some other methods, and works well for lots of different types of problems. |
Keywords
» Artificial intelligence » Optimization
