A global minimum refers to the lowest point in a mathematical function over its entire domain. In optimization problems, finding the global minimum is crucial because it represents the best possible solution, minimizing costs or maximizing efficiency. This concept is often used in fields like economics, engineering, and machine learning. In contrast to local minima, which are the lowest points within a specific region, a global minimum is the absolute lowest point across all possible values. Identifying global minima can be challenging, especially in complex functions with multiple local minima, making advanced algorithms and techniques necessary for effective optimization.