What are the limitations of Bayesian optimization?

What are the limitations of Bayesian optimization?

Bayesian optimization is powerful, but it has limitations like scalability, computational cost, and sensitivity to prior assumptions.

Can you explain how scalability affects its performance?

Scalability is an issue because Bayesian optimization can struggle with high-dimensional spaces, leading to inefficiencies.

What about the computational cost? Is it always high?

Not always, but the cost can increase significantly with the number of evaluations and the complexity of the model.

How does sensitivity to prior assumptions impact the results?

If the prior assumptions are incorrect, it can lead to suboptimal results, making the optimization less reliable.

Are there any alternatives to Bayesian optimization?

Yes, alternatives include genetic algorithms, particle swarm optimization, and gradient-based methods, each with its pros and cons.