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We consider the dictionary learning problem in sparse representations based on an analysis model with noisy observations. A typical limitation associated with several existing analysis dictionary learning (ADL) algorithms, such as Analysis K-SVD, is their slow convergence due to the procedure used to pre-estimate the source signal from the noisy measurements when updating the dictionary atoms in each...
In this paper, we will present an effective improved parallel hybrid asynchronous preconditioned GMRES method implemented on a nation wide grid environment. The classic restarted GMRES method is used widely to solve the large sparse linear systems. In order to accelerate the convergence, we use Arnoldi method to compute Ritz elements in parallel to optimize the computation of a polynomial. These two...
The method GMRES is used widely to solve the large sparse linear systems. In this paper, we will present an effective parallel hybrid asynchronous method, which combines the typical parallel method GMRES with the Least Square method that needs some eigenvalues obtained from a parallel Arnoldi process. And we will apply it on a Grid Computing platform Grid5000. Grid computing in general is a special...
Considering the deficiency of the traditional crossover operator of Genetic Algorithms (GAs) in global searching, the partheno-crossover operator is proposed in this paper. The partheno-crossover, a new crossover operator, still uses the traditional method of crossover. The individual of the population doesn't cross over the individual of the same population but the individual of the completely different...
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