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» On Wiedemann's Method of Solving Sparse Linear Systems
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COLT
2004
Springer
14 years 2 months ago
Regularization and Semi-supervised Learning on Large Graphs
We consider the problem of labeling a partially labeled graph. This setting may arise in a number of situations from survey sampling to information retrieval to pattern recognition...
Mikhail Belkin, Irina Matveeva, Partha Niyogi
FGCS
2008
127views more  FGCS 2008»
13 years 9 months ago
Monte Carlo methods for matrix computations on the grid
Many scientific and engineering applications involve inverting large matrices or solving systems of linear algebraic equations. Solving these problems with proven algorithms for d...
Simon Branford, Cihan Sahin, Ashish Thandavan, Chr...
SIAMSC
2008
147views more  SIAMSC 2008»
13 years 9 months ago
Global and Finite Termination of a Two-Phase Augmented Lagrangian Filter Method for General Quadratic Programs
We present a two-phase algorithm for solving large-scale quadratic programs (QPs). In the first phase, gradient-projection iterations approximately minimize an augmented Lagrangian...
Michael P. Friedlander, Sven Leyffer
CIMAGING
2009
120views Hardware» more  CIMAGING 2009»
13 years 10 months ago
Dantzig selector homotopy with dynamic measurements
The Dantzig selector is a near ideal estimator for recovery of sparse signals from linear measurements in the presence of noise. It is a convex optimization problem which can be r...
Muhammad Salman Asif, Justin K. Romberg
ICASSP
2011
IEEE
13 years 25 days ago
Basis Pursuit in sensor networks
Basis Pursuit (BP) finds a minimum ℓ1-norm vector z that satisfies the underdetermined linear system Mz = b, where the matrix M and vector b are given. Lately, BP has attracte...
João F. C. Mota, João M. F. Xavier, ...