Nonconvex optimization involves finding the global minimum or maximum of a function that does not satisfy the properties of convexity, making it a challenging problem due to the presence of multiple local minima and maxima. These problems are prevalent in various fields such as machine learning, economics, and engineering, where traditional convex optimization techniques may not be applicable or efficient.