Reconstruction of Sparse Polynomials via Quasi-Orthogonal Matching Pursuit Method

Authors

  • Renzhong Feng School of Mathematics Science & Key Laboratory of Mathematics, Informatics and Behavioral Semantics, Ministry of Education, Beihang University, Beijing, China
  • Aitong Huang School of Mathematics Science & Key Laboratory of Mathematics, Informatics and Behavioral Semantics, Ministry of Education, Beihang University, Beijing, China
  • Ming-Jun Lai Department of Mathematics, University of Georgia, Athens, GA 30602, U.S.A.
  • Zhaiming Shen Department of Mathematics, University of Georgia, Athens, GA 30602, U.S.A.

DOI:

https://doi.org/10.4208/jcm.2104-m2020-0250

Keywords:

Reconstruction of sparse polynomial, Compressive sensing, Mutual coherence, Quasi-orthogonal matching pursuit algorithm.

Abstract

In this paper, we propose a Quasi-Orthogonal Matching Pursuit (QOMP) algorithm for constructing a sparse approximation of functions in terms of expansion by orthonormal polynomials. For the two kinds of sampled data, data with noises and without noises, we apply the mutual coherence of measurement matrix to establish the convergence of  the QOMP algorithm which can reconstruct $s$-sparse Legendre polynomials, Chebyshev polynomials and  trigonometric polynomials in $s$ step iterations. The results are also extended to general bounded orthogonal system including tensor product of these three univariate orthogonal polynomials. Finally, numerical experiments will be presented to verify the effectiveness of the QOMP method.

Published

2022-12-01

Issue

Section

Articles