Nonlinear basis pursuit
Henrik Ohlsson, Allen Y. Yang, Roy Dong, S. Shankar Sastry
Abstract
Open-access reader
Henrik Ohlsson, Allen Y. Yang, Roy Dong, S. Shankar Sastry
Abstract
Open-access reader
In compressive sensing, the basis pursuit algorithm aims to find the sparsest solution to an underdetermined linear equation system. In this paper, we generalize basis pursuit to finding the sparsest solution to higher order nonlinear systems of equations, called nonlinear basis pursuit. In contrast to the existing nonlinear compressive sensing methods, the new algorithm is based on convex relaxation and is not a greedy method. The novel algorithm enables the compressive sensing approach to be used for a broader range of applications where there are nonlinear relationships between the measurements and the unknowns.
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In compressive sensing, the basis pursuit algorithm aims to find the sparsest solution to an underdetermined linear equation system. In this paper, we generalize basis pursuit to finding the sparsest solution to higher order nonlinear systems of equations, called nonlinear basis pursuit. In contrast to the existing nonlinear compressive sensing methods, the new algorithm is based on convex relaxation and is not a greedy method. The novel algorithm enables the compressive sensing approach to be used for a broader range of applications where there are nonlinear relationships between the measurements and the unknowns.
Key concepts: Basis pursuit, Underdetermined system, Compressed sensing, Nonlinear system, Basis (linear algebra), Greedy algorithm, Matching pursuit, Mathematics