Sparse Approximation is Provably Hard under Coherent Dictionaries

It is well known that sparse approximation problem is \textsf{NP}-hard under general dictionaries. Several algorithms have been devised and analyzed in the past decade under various assumptions on the \emph{coherence} $μ$ of the dictionary represented by an $M \times N$ matrix from which a subset of $k$ column vectors is selected. All these results assume $μ=O(k^{-1})$. This article is an attempt to bridge the big gap between the negative result of \textsf{NP}-hardness under general dictionaries and the positive results under this restrictive assumption. In particular, it suggests that the aforementioned assumption might be asymptotically the best one can make to arrive at any efficient algorithmic result under well-known conjectures of complexity theory. In establishing the results, we make use of a new simple multilayered PCP which is tailored to give a matrix with small coherence combined with our reduction.