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HPCN
1997
Springer
13 years 11 months ago
Parallel Solution of Irregular, Sparse Matrix Problems Using High Performance Fortran
For regular, sparse, linear systems, like those derived from regular grids, using High Performance Fortran (HPF) for iterative solvers is straightforward. However, for irregular ma...
Eric de Sturler, Damian Loher
SDM
2008
SIAM
139views Data Mining» more  SDM 2008»
13 years 8 months ago
Semi-Supervised Learning Based on Semiparametric Regularization
Semi-supervised learning plays an important role in the recent literature on machine learning and data mining and the developed semisupervised learning techniques have led to many...
Zhen Guo, Zhongfei (Mark) Zhang, Eric P. Xing, Chr...
ITSL
2008
13 years 8 months ago
An Empirical Comparison of NML Clustering Algorithms
Clustering can be defined as a data assignment problem where the goal is to partition the data into nonhierarchical groups of items. In our previous work, we suggested an informati...
Petri Kontkanen, Petri Myllymäki
FOCS
2002
IEEE
14 years 10 days ago
Implicit B-Trees: New Results for the Dictionary Problem
We reopen the issue of finding an implicit data structure for the dictionary problem. In particular, we examine the problem of maintaining Ò data values in the first Ò locatio...
Gianni Franceschini, Roberto Grossi, J. Ian Munro,...
ISHPC
1997
Springer
13 years 11 months ago
Implementing Iterative Solvers for Irregular Sparse Matrix Problems in High Performance Fortran
Abstract. Writing e cient iterative solvers for irregular, sparse matrices in HPF is hard. The locality in the computations is unclear, and for e ciency we use storage schemes that...
Eric de Sturler, Damian Loher