The objective function of Principle Component Analysis (PCA) is minimizing the reconstruction error in L2 norm (see section 2.12 here. Another view is trying to maximize the variance on projection. We also have an excellent post here: What is the objective function of PCA?).

My question is that Is PCA optimization convex? (I found some discussions here, but wish someone can provide nice proof in CV)