We propose the Non-negative Quadratic Pursuit (NQP) algorithm to approximately minimize a quadratic function in the presence of the $l0$-norm constraint. It is a quadratic generalization of matching pursuit method which is reformulated in a non-negative framework. Although the optimization problem is NP-hard, NQP provides an approximate solution to the problem which is locally optimal, but acceptable in the general literature. In this document, we explain the algorithm's exact steps along with its convergence proof and complexity analysis.
Comments: 4 Pages. Pre-print introduction for a section of recently submitted article, as provided by the authors.
[v1] 2018-05-29 04:16:47
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