Global optimization is a mathematical approach aimed at finding the best solution from all possible solutions in a given problem space. Unlike local optimization, which focuses on finding the best solution within a limited area, global optimization seeks the absolute best solution across the entire domain. This is particularly important in complex problems where multiple local optima may exist. Techniques for global optimization include methods like genetic algorithms, simulated annealing, and particle swarm optimization. These methods are used in various fields, such as engineering, finance, and machine learning, to improve decision-making and enhance performance by identifying optimal solutions.