Non-convex optimization involves finding the global minimum or maximum of a problem where the objective function or constraints are non-convex, often leading to multiple local optima. This makes it inherently more challenging than convex optimization, requiring advanced techniques like heuristic methods, stochastic approaches, or global optimization strategies to navigate complex solution landscapes.