This paper mainly provides a method to evaluate the contribution rate of equipment system based on machine learning. Firstly, the index system of contribution rate evaluation of equipment system is established, the re...
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We present a scalable approach to solve a class of elliptic partial differential equation (PDE)-constrained optimization problems with bound constraints. This approach utilizes a robust full-space interior-point (IP)-...
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We present a scalable approach to solve a class of elliptic partial differential equation (PDE)-constrained optimization problems with bound constraints. This approach utilizes a robust full-space interior-point (IP)-Gauss-Newton optimization method. To cope with the poorly-conditioned IP-Gauss-Newton saddle-point linear systems that need to be solved, once per optimization step, we propose two spectrally related preconditioners. These preconditioners leverage the limited informativeness of data in regularized PDE-constrained optimization problems. A block Gauss-Seidel preconditioner is proposed for the GMRES-based solution of the IP-Gauss-Newton linear systems. It is shown, for a large-class of PDE- and bound-constrained optimization problems, that the spectrum of the block Gauss-Seidel preconditioned IP-Gauss-Newton matrix is asymptotically independent of discretization and is not impacted by the ill-conditioning that notoriously plagues interior-point methods. We exploit symmetry of the IP-Gauss-Newton linear systems and propose a regularization and log-barrier Hessian preconditioner for the preconditioned conjugate gradient (PCG)based solution of the related IP-Gauss-Newton-Schur complement linear systems. The eigenvalues of the block Gauss-Seidel preconditioned IP-Gauss-Newton matrix, that are not equal to one, are identical to the eigenvalues of the regularization and log-barrier Hessian preconditioned Schur complement matrix. The scalability of the approach is demonstrated on an example problem with bound and nonlinear elliptic PDE constraints. The numerical solution of the optimization problem is shown to require a discretization independent number of IP-Gauss-Newton linear solves. Furthermore, the linear systems are solved in a discretization and IP ill-conditioning independent number of preconditioned Krylov subspace iterations. The parallel scalability of preconditioner and linear system matrix applies, achieved with algebraic multigrid based solvers, and
This paper focuses on the requirements of operational efficiency evaluation for UAV cluster, and establishes an evaluation index system for UAV cluster operations, and establishes evaluation index system from the aspe...
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When datasets have missing parts, we typically use data characteristics to select a suitable model to generate these missing parts. However, the poor quality of real data often affects the fitting accuracy of the idea...
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A plethora of machine learning methods have been applied to imaging data, enabling the construction of clinically relevant imaging signatures of neurological and neuropsychiatric diseases. Oftentimes, such methods do ...
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In this paper, we explored different ways to write the algebraic version of betweenness centrality algorithm. Particularly, we focused on Brandes’ algorithm [8]. We aimed for algebraic betweenness centrality that can...
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In this work, we consider the non-invasive medical imaging modality of Electrical Impedance Tomography, where the problem is to recover the conductivity in a medium from a set of data that arises out of a current-to-v...
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Localisation limits and nonlocal approximations of degenerate parabolic systems have experienced a renaissance in recent years. However, only few results cover anisotropic systems. This work addresses this gap by esta...
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We present QCAM, a quantum analogue of Content-Addressable Memory (CAM), useful for finding matches in two sequences of bit-strings. Our QCAM implementation takes advantage of Grover's search algorithm and propose...
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Neutrosophic set (NS) is a framework used when the imprecision and uncertainty of an event are described based on three possible aspects, i.e., the membership degree, neutral membership degree and non-membership degre...
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