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» Randomized Methods for Linear Constraints: Convergence Rates...
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SIAMSC
2010
143views more  SIAMSC 2010»
13 years 5 months ago
Computing and Deflating Eigenvalues While Solving Multiple Right-Hand Side Linear Systems with an Application to Quantum Chromod
Abstract. We present a new algorithm that computes eigenvalues and eigenvectors of a Hermitian positive definite matrix while solving a linear system of equations with Conjugate G...
Andreas Stathopoulos, Konstantinos Orginos
SIAMJO
2010
83views more  SIAMJO 2010»
13 years 5 months ago
The Lifted Newton Method and Its Application in Optimization
Abstract. We present a new “lifting” approach for the solution of nonlinear optimization problems (NLPs) that have objective and constraint functions with intermediate variable...
Jan Albersmeyer, Moritz Diehl
IPSN
2004
Springer
14 years 27 days ago
Estimation from lossy sensor data: jump linear modeling and Kalman filtering
Due to constraints in cost, power, and communication, losses often arise in large sensor networks. The sensor can be modeled as an output of a linear stochastic system with random...
Alyson K. Fletcher, Sundeep Rangan, Vivek K. Goyal
ICASSP
2009
IEEE
14 years 2 months ago
Sparse source separation from orthogonal mixtures
This paper addresses source separation from a linear mixture under two assumptions: source sparsity and orthogonality of the mixing matrix. We propose efficient sparse separation...
Moshe Mishali, Yonina C. Eldar
NIPS
1996
13 years 8 months ago
Radial Basis Function Networks and Complexity Regularization in Function Learning
In this paper we apply the method of complexity regularization to derive estimation bounds for nonlinear function estimation using a single hidden layer radial basis function netwo...
Adam Krzyzak, Tamás Linder