A Gradient-Enhanced ℓ_(1)Approach for the Recovery of Sparse Trigonometric Polynomials  

作　　者：Zhiqiang Xu Tao Zhou 

机构地区：[1]LSEC,Institute of Computational Mathematics,Academy of Mathematics and Systems Science,Chinese Academy of Sciences,Beijing,China.

出　　处：《Communications in Computational Physics》2018年第6期286-308,共23页计算物理通讯（英文）

基　　金：Zhiqiang Xuwas supported by NSFC grant(91630203,11422113,11331012,11688101);by National Basic Research Program of China(973 Program 2015CB856000);Tao Zhou was supported by the NSF of China(under grant numbers 11688101,91630312,91630203,11571351,and 11731006);the science challenge project(No.TZ2018001),NCMIS,and the youth innovation promotion association(CAS).

摘　　要：In this paper,we discuss a gradient-enhancedℓ_(1)approach for the recovery of sparse Fourier expansions.By gradient-enhanced approaches we mean that the directional derivatives along given vectors are utilized to improve the sparse approximations.We first consider the case where both the function values and the directional derivatives at sampling points are known.We show that,under some mild conditions,the inclusion of the derivatives information can indeed decrease the coherence of measurementmatrix,and thus leads to the improved the sparse recovery conditions of theℓ_(1)minimization.We also consider the case where either the function values or the directional derivatives are known at the sampling points,in which we present a sufficient condition under which the measurement matrix satisfies RIP,provided that the samples are distributed according to the uniform measure.This result shows that the derivatives information plays a similar role as that of the function values.Several numerical examples are presented to support the theoretical statements.Potential applications to function(Hermite-type)interpolations and uncertainty quantification are also discussed.

关 键 词：Gradient-enhancedℓ_(1)minimization compressed sensing sparse Fourier expansions restricted isometry property mutual incoherence 

分 类 号：O62[理学—有机化学]