Many scienti c applications require sparse matrix computations. For example, Finite Element modeling and N-body simulations. It is di cult to write these codes in a portable way which also achieves high performance because of the sparsity of the matrices and because current architectures have deep memory hierarchies and multiple levels of parallelism. Therefore the implementation of such comput...

Many scientiic applications require sparse matrix computations. For example, Finite Element model-ing and N-body simulations. It is diicult to write these codes in a portable way which also achieves high performance because of the sparsity of the matrices and because current architectures have deep memory hierarchies and multiple levels of parallelism. Therefore the implementation of such compu...

Many applications invoke the Fast Fourier Transform (FFT) on sparse inputs, with most of their Fourier coefficients being very small or equal to zero. Compared with the “dense” FFT algorithms, the input sparsity makes it easier to parallelize the sparse counterparts. In general, sparse FFT algorithms filter input into different frequency bins, and then process the bins separately. Clearly, the ...

Compressed sensing is a new sampling technique which can exactly reconstruct sparse signal from a few measurements. In this article, we consider the blocksparse compressed sensing with special structure assumption about the signal. A novel non-convex model is proposed to reconstruct the block-sparse signals. In addition, the conditions of the proposed model for recovering the block-sparse noise...

Abstract We prove a quadratic sparse domination result for general non-integral square functions S . That is, $$p_0 \in [1,2)$$ p 0 ∈ [ 1 , 2 ) a...

Abstract In this paper, we derive a new reconstruction method for real non-harmonic Fourier sums, i.e., signals which can be represented as sparse exponential sums of the form $$f(t) = \sum _{j=1}^{K} \gamma _{j} \, \cos (2\pi a_{j} t + b_{j})$$ f(t)</...