Multivariable Polynomials for the Construction of Binary Sensing Matrices

R, Ramu Naidu and Jampana, Phanindra Varma and Challa, Subrahmanya Sastry (2016) Multivariable Polynomials for the Construction of Binary Sensing Matrices. In: Forging Connections between Computational Mathematics and Computational Geometry: Papers from the 3rd International Conference on Computational Mathematics and Computational Geometry. Springer Proceedings in Mathematics & Statistics, 124 . Springer International Publishing, pp. 53-61. ISBN 978-3-319-16138-9

Full text not available from this repository. (Request a copy)

Abstract

In compressed sensing, the matrices that satisfy restricted isometry property (RIP) play an important role. But to date, very few results for designing such matrices are available. Of interest in several applications is a matrix whose elements are 0’s and 1’s (in short, 0, 1-matrix), excluding column normalization factors. Recently, DeVore (J Complex 23:918–925, 2007) has constructed deterministic 0, 1-matrices that obey sparse recovery properties such as RIP. The present work extends the ideas embedded in DeVore (J Complex 23:918–925, 2007) and shows that the 0, 1-matrices of different sizes can be constructed using multivariable homogeneous polynomials.

[error in script]

IITH Creators: