Sparsity-Constrained Optimization of Inputs to Second-Order Systems

We propose an efficient algorithm, that given a strictly proper, second-order system, finds a sparse input signal so that the system's output optimally approximates a given trajectory in least-squares sense. As an illustration, we apply the algorithm to an estimation problem from medicine.