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TIP
2010
162views more  TIP 2010»
13 years 2 months ago
Super-Resolution With Sparse Mixing Estimators
We introduce a class of inverse problem estimators computed by mixing adaptively a family of linear estimators corresponding to different priors. Sparse mixing weights are calcula...
Stéphane Mallat, Guoshen Yu
ORL
2011
13 years 2 months ago
Convex approximations to sparse PCA via Lagrangian duality
We derive a convex relaxation for cardinality constrained Principal Component Analysis (PCA) by using a simple representation of the L1 unit ball and standard Lagrangian duality. ...
Ronny Luss, Marc Teboulle
PPAM
2005
Springer
14 years 1 months ago
A New Diagonal Blocking Format and Model of Cache Behavior for Sparse Matrices
Algorithms for the sparse matrix-vector multiplication (shortly SpM×V ) are important building blocks in solvers of sparse systems of linear equations. Due to matrix sparsity, the...
Pavel Tvrdík, Ivan Simecek
COMPGEOM
2006
ACM
14 years 1 months ago
How to get close to the median shape
In this paper, we study the problem of L1-fitting a shape to a set of point, where the target is to minimize the sum of distances of the points to the shape, or alternatively the...
Sariel Har-Peled
WADS
2005
Springer
132views Algorithms» more  WADS 2005»
14 years 1 months ago
Communication-Aware Processor Allocation for Supercomputers
Abstract. We give processor-allocation algorithms for grid architectures, where the objective is to select processors from a set of available processors to minimize the average num...
Michael A. Bender, David P. Bunde, Erik D. Demaine...