Summary of Single Point-based Distributed Zeroth-order Optimization with a Non-convex Stochastic Objective Function, by Elissa Mhanna and Mohamad Assaad
Single Point-Based Distributed Zeroth-Order Optimization with a Non-Convex Stochastic Objective Function
by Elissa Mhanna, Mohamad Assaad
First submitted to arxiv on: 8 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 The proposed zero-order distributed optimization method combines the benefits of zero-order and gradient-tracking techniques to efficiently converge in non-convex settings with minimal knowledge of gradients. Building upon one-point estimates, this novel approach requires only noisy function queries at each iteration, demonstrating competitive convergence rates compared to centralized methods. Theoretical analysis shows a convergence rate of O(1/√[3]K) after K iterations, validated through numerical examples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way for computers to work together and find the best solution when there are many constraints. It combines two existing techniques to make it more efficient and accurate. The method only needs to know the value of the function at one point, making it useful when we don’t have enough information about the gradient. The results show that this new approach works well even in complex problems. |
Keywords
» Artificial intelligence » Optimization » Tracking
