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» Run-Time Techniques for Parallelizing Sparse Matrix Problems
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ICCAD
1997
IEEE
108views Hardware» more  ICCAD 1997»
13 years 11 months ago
Negative thinking by incremental problem solving: application to unate covering
We introduce a new technique to solve exactly a discrete optimization problem, based on the paradigm of “negative” thinking. The motivation is that when searching the space of...
Evguenii I. Goldberg, Luca P. Carloni, Tiziano Vil...
CVPR
2011
IEEE
13 years 2 months ago
High-resolution Hyperspectral Imaging via Matrix Factorization
Hyperspectral imaging is a promising tool for applications in geosensing, cultural heritage and beyond. However, compared to current RGB cameras, existing hyperspectral cameras ar...
Rei Kawakami, John Wright, Yu-Wing Tai, Yasuyuki M...
ALGORITHMICA
2005
195views more  ALGORITHMICA 2005»
13 years 7 months ago
Bit-Parallel Witnesses and Their Applications to Approximate String Matching
We present a new bit-parallel technique for approximate string matching. We build on two previous techniques. The first one, BPM [Myers, J. of the ACM, 1999], searches for a patte...
Heikki Hyyrö, Gonzalo Navarro
ICPP
2002
IEEE
14 years 13 days ago
ART: Robustness of Meshes and Tori for Parallel and Distributed Computation
In this paper, we formulate the array robustness theorems (ARTs) for efficient computation and communication on faulty arrays. No hardware redundancy is required and no assumptio...
Chi-Hsiang Yeh, Behrooz Parhami
CORR
2010
Springer
93views Education» more  CORR 2010»
13 years 7 months ago
Rank Awareness in Joint Sparse Recovery
In this paper we revisit the sparse multiple measurement vector (MMV) problem, where the aim is to recover a set of jointly sparse multichannel vectors from incomplete measurement...
Mike E. Davies, Yonina C. Eldar