What is the difference between autoencoders and PCA in optimization objective?

PCA minimizes linear reconstruction error; autoencoders minimize nonlinear reconstruction error.

PCA solves a constrained linear optimization problem maximizing variance and minimizing squared reconstruction error in a linear subspace. Autoencoders generalize this by using nonlinear neural networks, allowing them to model complex manifolds but requiring gradient-based optimization.