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» Run-Time Techniques for Parallelizing Sparse Matrix Problems
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CORR
2011
Springer
148views Education» more  CORR 2011»
14 years 4 months ago
How well can we estimate a sparse vector?
The estimation of a sparse vector in the linear model is a fundamental problem in signal processing, statistics, and compressive sensing. This paper establishes a lower bound on t...
Emmanuel J. Candès, Mark A. Davenport
LSSC
2001
Springer
15 years 2 months ago
On the Parallelization of the Sparse Grid Approach for Data Mining
Abstract. Recently we presented a new approach [5, 6] to the classification problem arising in data mining. It is based on the regularization network approach, but in contrast to ...
Jochen Garcke, Michael Griebel
SC
2009
ACM
15 years 2 months ago
GPU based sparse grid technique for solving multidimensional options pricing PDEs
It has been shown that the sparse grid combination technique can be a practical tool to solve high dimensional PDEs arising in multidimensional option pricing problems in finance...
Abhijeet Gaikwad, Ioane Muni Toke
SIAMSC
2010
142views more  SIAMSC 2010»
14 years 4 months ago
Hypergraph-Based Unsymmetric Nested Dissection Ordering for Sparse LU Factorization
In this paper we present HUND, a hypergraph-based unsymmetric nested dissection ordering algorithm for reducing the fill-in incurred during Gaussian elimination. HUND has several i...
Laura Grigori, Erik G. Boman, Simplice Donfack, Ti...
SIAMJO
2011
14 years 4 months ago
Recovering Low-Rank and Sparse Components of Matrices from Incomplete and Noisy Observations
Many applications arising in a variety of fields can be well illustrated by the task of recovering the low-rank and sparse components of a given matrix. Recently, it is discovered...
Min Tao, Xiaoming Yuan