Non-smooth optimization refers to optimization problems where the objective function or constraints are not differentiable, often requiring specialized methods for finding optimal solutions. These problems commonly arise in real-world applications such as machine learning, economics, and engineering, where traditional gradient-based methods may fail to converge or provide suboptimal solutions.