Summary of Online Convex Optimization with a Separation Oracle, by Zakaria Mhammedi
Online Convex Optimization with a Separation Oracle
by Zakaria Mhammedi
First submitted to arxiv on: 3 Oct 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 Our new algorithm for Online Convex Optimization (OCO) achieves a state-of-the-art regret guarantee among separation-based algorithms. Unlike previous projection-free methods that achieve a suboptimal regret bound of O(T^(3/4)), our algorithm guarantees a regret bound of O(sqrt(dT) + kappad), where kappa denotes the asphericity of the feasible set. This improvement addresses limitations in previous methods, particularly for ill-conditioned sets where kappa can be arbitrarily large. Our analysis also recovers the O(kappasqrt(T)) regret bound of existing OCO algorithms and improves the regret bound for projection-free online exp-concave optimization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Our new algorithm is a simple and effective way to solve Online Convex Optimization problems. It works by using a clever combination of mathematical techniques to find the best solution. This approach has many practical applications, such as optimizing the performance of computer networks or managing supply chains. The algorithm’s main advantage is that it can handle complex optimization problems quickly and efficiently. |
Keywords
* Artificial intelligence * Optimization
