  We present an algorithm for minimizing the sum of a strongly convex
time-varying function with a time-invariant, convex, and nonsmooth function.
The proposed algorithm employs the prediction-correction scheme alongside the
forward-backward envelope, and we are able to prove the convergence of the
solutions to a neighborhood of the optimizer that depends on the sampling time.
Numerical simulations for a time-varying regression problem with elastic net
regularization highlight the effectiveness of the algorithm.
