I'm looking for algorithm implementations that can solve problems of the type
$$\min \sum_i f_i(x_i) \\ s.t. Ax \leq b \\ Cx = d \\ x_i \in [\ell_i,u_i]$$
I.e. polyhedrally constrained optimization problems with a separably convex objective function (NOT necessarily strictly convex and NOT necessarily differentiable). The reason I'm asking is because I've done a research project in which I've developed some new algorithms for this exact type of problem and I'd like some sort of benchmark. It's proving hard to find ready implementations and I'd prefer to not implement it from scratch (I'm rather short on time).
In particular, augmented Lagrangian methods or proximal methods would be very nice. Does anyone have any ideas where to look?