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Related Questions
- What is the main difference between Bayesian optimization and random search in the context of high-dimensional optimization problems?
- How does Bayesian optimization adapt to the search space as it explores, and what are its implications for the optimization process?
- What are the computational costs associated with Bayesian optimization versus random search, and how do they impact the choice of method?
- Can Bayesian optimization be used for problems with non-convex or noisy objective functions, and how does it handle these challenges?
- How does Bayesian optimization balance exploration and exploitation, and what are the implications for the optimization process?
- What are the advantages of Bayesian optimization in terms of convergence speed and solution quality compared to random search?
- Can random search be used as a baseline or initialization method for Bayesian optimization, and what are the benefits of this approach?
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