No Free Lunch Theorems

The No Free Lunch (NFL) Theorems state that, when averaged over all possible problems, no single algorithm outperforms others universally. In other words, an algorithm's effectiveness depends on the specific problem it's applied to. An approach that works well for one type of problem may perform poorly on another. Therefore, there's no one-size-fits-all solution in optimization or machine learning—success requires choosing tools suited to each task. This highlights the importance of understanding problem context and tailoring methods accordingly.