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» Solving Sparse Linear Constraints
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ICASSP
2011
IEEE
14 years 2 months ago
Estimation and dynamic updating of time-varying signals with sparse variations
This paper presents an algorithm for an 1-regularized Kalman filter. Given observations of a discrete-time linear dynamical system with sparse errors in the state evolution, we e...
Muhammad Salman Asif, Adam Charles, Justin K. Romb...
PVM
1999
Springer
15 years 2 months ago
Parallel Monte Carlo Algorithms for Sparse SLAE Using MPI
The problem of solving sparse Systems of Linear Algebraic Equations (SLAE) by parallel Monte Carlo numerical methods is considered. The almost optimal Monte Carlo algorithms are pr...
Vassil N. Alexandrov, Aneta Karaivanova
ICA
2007
Springer
15 years 2 months ago
Estimating the Mixing Matrix in Sparse Component Analysis Based on Converting a Multiple Dominant to a Single Dominant Problem
We propose a new method for estimating the mixing matrix, A, in the linear model x(t) = As(t), t = 1, . . . , T, for the problem of underdetermined Sparse Component Analysis (SCA)....
Nima Noorshams, Massoud Babaie-Zadeh, Christian Ju...
115
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TSP
2010
14 years 5 months ago
Methods for sparse signal recovery using Kalman filtering with embedded pseudo-measurement norms and quasi-norms
We present two simple methods for recovering sparse signals from a series of noisy observations. The theory of compressed sensing (CS) requires solving a convex constrained minimiz...
Avishy Carmi, Pini Gurfil, Dimitri Kanevsky
VALUETOOLS
2006
ACM
176views Hardware» more  VALUETOOLS 2006»
15 years 4 months ago
How to solve large scale deterministic games with mean payoff by policy iteration
Min-max functions are dynamic programming operators of zero-sum deterministic games with finite state and action spaces. The problem of computing the linear growth rate of the or...
Vishesh Dhingra, Stephane Gaubert