This paper deals with approaches for sparse matrix substitutions using vector processing. Many publications have used the W-matrix method to solve the forward/backward substitutions on vector computer. Recently a different ...

This paper deals with approaches for sparse matrix substitutions using vector processing. Many publications have used the W-matrix method to solve the forward/backward substitutions on vector computer. Recently a different ...

This paper describes a methodology for solving a linear system of equations on vector computer. The methodology combines direct and inverse factors. The decomposition and implementation of the direct solution in a CRAY ...

In this paper, we consider sparse representations of multidimensional signals (tensors) by generalizing the one-dimensional case (vectors). A new greedy algorithm, namely the Tensor-OMP algorithm, is proposed to compute a ...

We obtain a sparse domination principle for an arbitrary family of functions f(x,Q) , where x∈Rn and Q is a cube in Rn . When applied to operators, this result recovers our recent works [37, 39]. On the other hand, our ...

The modern GPUs are well suited for intensive computational tasks and massive parallel computation. Sparse matrix multiplication and linear triangular solver are the most important and heavily used kernels in scientific ...

In this paper some new results concerning the Cp classes introduced by Muckenhoupt (1981) and later extended by Sawyer (1983), are provided. In particular, we extend the result to the full expected range p < 0, to the weak ...

Recently, there is a great interest in sparse representations of signals under the assumption that signals (datasets) can be well approximated by a linear combination of few elements of a known basis (dictionary). Many ...