Comparing Bayesian Optimization with Other Optimization Methods
Understand the strengths and limitations of Bayesian optimization compared to other optimization methods such as gradient descent, random search, evolutionary algorithms, and grid search. Learn how Bayesian optimization's global scope, efficiency, and model-based approach make it suitable for complex, high-dimensional optimization problems and hyperparameter tuning in machine learning.
Bayesian optimization is a popular and effective method for global optimization of expensive black box functions. The following presents a brief comparison of Bayesian optimization with other methods.
Bayesian optimization vs. gradient descent
Differentiability: Gradient descent methods require the objective function to be differentiable, while Bayesian optimization doesn’t have this requirement, which makes it suitable for optimizing a wider range of functions.
Evaluation cost: Bayesian optimization is ideal for expensive black box functions because it seeks to minimize the number of function evaluations. Gradient descent ...