Upper Bounds on the Error of Sparse Vector and Low-Rank Matrix Recovery

Suppose that a solution x̃ to an underdetermined linear system b = Ax is given. x̃ is approximately sparse meaning that it has a few large components compared to other small entries. However, the total number of nonzero components of x̃ is large enough to violate any condition for the uniqueness of the sparsest solution. On the other hand, if only the dominant components are considered, then it will satisfy the uniqueness conditions. One intuitively expects that x̃ should not be far from the true sparse solution x0. We show that this intuition is the case by providing an upper bound on ‖̃x − x0‖ which is a function of the magnitudes of small components of x̃ but independent from x0. This result is extended to the case that b is perturbed by noise. Additionally, we generalize the upper bounds to the low-rank matrix recovery problem.