Proximal algorithms for nuclear norm system identification

In this thesis, we present four proximal algorithms for the solution of a nuclear norm optimization problem. Respect to other papers, we solve a more generic version of this problem. Another contribution is related in particular to the model order selection, in fact, we propose to use the parsimony principle. Experimentally with this method, we obtain a better fit with a lower model order.