Comput Math Methods Med - Linear program relaxation of sparse nonnegative recovery in compressive sensing microarrays.

Tópicos

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Resumo

Compressive sensing microarrays (CSM) are DNA-based sensors that operate using group testing and compressive sensing principles. Mathematically, one can cast the CSM as sparse nonnegative recovery (SNR) which is to find the sparsest solutions subjected to an underdetermined system of linear equations and nonnegative restriction. In this paper, we discuss the l1 relaxation of the SNR. By defining nonnegative restricted isometry/orthogonality constants, we give a nonnegative restricted property condition which guarantees that the SNR and the l1 relaxation share the common unique solution. Besides, we show that any solution to the SNR must be one of the extreme points of the underlying feasible set.

Resumo Limpo

compress sens microarray csm dnabas sensor oper use group test compress sens principl mathemat one can cast csm spars nonneg recoveri snr find sparsest solut subject underdetermin system linear equat nonneg restrict paper discuss l relax snr defin nonneg restrict isometryorthogon constant give nonneg restrict properti condit guarante snr l relax share common uniqu solut besid show solut snr must one extrem point under feasibl set

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