In sparse principal component analysis we are given noisy observations of a
low-rank matrix of dimension $n\times p$ and seek to reconstruct it under
additional sparsity assumptions. In particular, we assume here each of the
principal components $\mathbf{v}_1,\dots,\mathbf{v}_r$ has at