Summary of Derivative-free Optimization Via Finite Difference Approximation: An Experimental Study, by Wang Du-yi and Liang Guo and Liu Guangwu and Zhang Kun
Derivative-Free Optimization via Finite Difference Approximation: An Experimental Study
by Wang Du-Yi, Liang Guo, Liu Guangwu, Zhang Kun
First submitted to arxiv on: 31 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 This paper investigates the effectiveness of two derivative-free optimization (DFO) methods for complex optimization problems with noisy function evaluations. The Kiefer-Wolfowitz (KW) and simultaneous perturbation stochastic approximation (SPSA) algorithms are compared to batch-based finite difference (FD) estimators, which approximate gradients more accurately but require more samples. The authors conduct extensive experiments in both low-dimensional and high-dimensional settings to examine the trade-off between gradient estimation accuracy and iteration steps. Surprisingly, they find that batch-based FD estimators with gradient descent outperform classical KW and SPSA algorithms in most scenarios. This study contributes to the understanding of DFO methods and their applications, shedding light on the fundamental question of which approach is more effective. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research compares two ways to solve complex optimization problems when we only have noisy data. The first method uses a clever trick to estimate gradients, while the second method takes many samples at once to get an accurate picture. The researchers tested these methods in simple and complex situations and found that taking many samples at once often leads to better results. This study helps us understand how to choose the best approach for different problems and applications. |
Keywords
» Artificial intelligence » Gradient descent » Optimization
