Sampling is a powerful technique in optimization that significantly reduces computational time by evaluating a subset of the candidate solutions instead of exhaustively searching the entire space. This approach allows for faster convergence and improved robustness, making it particularly valuable for complex optimization problems. Sampling-based optimization techniques such as Markov Chain Monte Carlo and Latin hypercube sampling have been successfully applied in various fields, including machine learning, image segmentation, and engineering design, demonstrating the transformative impact of sampling in modern optimization practices.
