This chapter approaches meta-learning through the lens of optimization theory. We frame the objective of learning to learn efficiently as a bilevel optimization problem: an outer optimization process guides an inner adaptation process. You will study the formal structure of this problem, examine algorithms designed to solve it (including gradient-based and implicit methods), see connections to hyperparameter optimization techniques, investigate strategies for finding optimal model initializations for rapid adaptation, and review theoretical results concerning the convergence of these methods. This perspective provides a structured mathematical foundation for understanding how meta-learning algorithms function and where their complexities lie.