👉 Boc Math, or Bayesian Optimization for Continuous Functions, is a powerful optimization technique used to find the maximum or minimum of a given continuous function efficiently, especially when evaluations of the function are expensive or time-consuming. It operates by constructing a probabilistic model, typically a Gaussian Process (GP), to approximate the objective function based on observed data points. This model predicts not only the function's value at new points but also its uncertainty, allowing the algorithm to intelligently select which points to evaluate next. By balancing exploration (sampling uncertain regions) and exploitation (sampling promising areas), Boc Math iteratively refines the search for the optimal point, making it particularly effective for complex, high-dimensional, or noisy landscapes. Its strength lies in its ability to converge to near-optimal solutions with fewer function evaluations compared to grid search or gradient-based methods, making it invaluable in fields like hyperparameter tuning and scientific computing.
