This thesis is concerned with convex optimization problems over matrix polynomials that are constrained to be positive semidefinite on the unit circle. Problems of this form appear in signal processing and can often be solved as semidefinite programs (SDPs). Interior-point solvers for these SDPs scale poorly, and this thesis aims to design first-order methods that are more efficient. We propose methods based on a generalized proximal operator defined in terms of a Bregman divergence. Empirical results on three applications in signal processing demonstrate that the proposed methods scale much better than interior-point solvers. As an example, for sparse estimation of spectral density matrices, Douglas--Rachford splitting with the generalized proximal operator is about 1000 times faster and scales to much larger problems. The ability to solve larger problems allows us to perform functional connectivity analysis of the brain by constructing a sparse estimate of the inverse spectral density matrix.