Cryo-electron microscopy single particle analysis (SPA) rep-resents a vital tool for structure determination of macro-molecules. Discrete inverseproblems arising in this field are extremely large and seriously contam...
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Cryo-electron microscopy single particle analysis (SPA) rep-resents a vital tool for structure determination of macro-molecules. Discrete inverseproblems arising in this field are extremely large and seriously contaminated by noise. The model matrix is highly structured and can not be stored ex-plicitly. Iterative regularization methods are used here only rarely, since it is believed that they are computationally ex-pensive and require complicated stopping criteria. In this paper, we overcome these difficulties and demonstrate that Hybrid Krylov subspace methods can be used to solve SPA inverseproblems efficiently. We propose an application-driven regularization parameter selection approach and present a matrix-free implementation of the hybrid solver for GPU com-putations in single precision arithmetic. In comparison to Fourier-based techniques, the hybrid approach allows to com-pute reconstructions from full (nonreduced) SPA models even for highly noisy data sets.(c) 2022 Elsevier Inc. All rights reserved.
We introduce a decomposition of the Tikhonov Regularization (TR) functional which split this operator into several TR functionals, suitably modified in order to enforce the matching of their solutions. As a consequenc...
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We introduce a decomposition of the Tikhonov Regularization (TR) functional which split this operator into several TR functionals, suitably modified in order to enforce the matching of their solutions. As a consequence, instead of solving one problem we can solve several problems reproducing the initial one at smaller dimensions. Such approach leads to a reduction of the time complexity of the resulting algorithm. Since the subproblems are solved in parallel, this decomposition also leads to a reduction of the overall execution time. Main outcome of the decomposition is that the parallel algorithm is oriented to exploit the highest performance of parallel architectures where concurrency is implemented both at the coarsest and finest levels of granularity. Performance analysis is discussed in terms of the algorithm and software scalability. Validation is performed on a reference parallel architecture made of a distributed memory multiprocessor and a Graphic Processing Unit. Results are presented on the Data Assimilation problem, for oceanographic models.
We present a numerical algorithm for solving largescale Tikhonov Regularization problems. The approach we consider introduces a splitting of the regularization functional which uses a domain decomposition, a partitio...
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ISBN:
(纸本)9783319321523;9783319321516
We present a numerical algorithm for solving largescale Tikhonov Regularization problems. The approach we consider introduces a splitting of the regularization functional which uses a domain decomposition, a partitioning of the solution and modified regularization functionals on each sub domain. We perform a feasibility analysis in terms of the algorithm and software scalability, to this end we use the scale-up factor which measures the performance gain in terms of time complexity reduction. We verify the reliability of the approach on a consistent test case (the Data Assimilation problem for oceanographic models).
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