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Showing 1 to 7 of 7 for “"large sparse matrices"”.
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Diagonal Estimation with Probing Methods
Probing methods for trace estimation of large, sparse matrices has been studied for several decades. In recent years, there has been some work to extend these techniques to instead estimate the diagonal entries of these systems directly. We extend some analysis of trace estimators to their …
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Hypergraph-Based Combinatorial Optimization of Matrix -Vector Multiplication
… is parallel matrix-vector multiplication for large sparse matrices. Parallel sparse matrix-vector multiplication is a particularly important numerical kernel in computational science. We have focused on optimizing the parallel performance of this operation by reducing the communication volume …
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Multiprocessor sparse SVD algorithms and applications
… the singular value decomposition (SVD) of large sparse matrices on a multiprocessor architecture. We particularly consider the SVD of unstructured sparse matrices in which the number of rows may be substantially larger or smaller than the number of columns. On vector machines, considerable …
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Ordering Strategies for Sparse Matrices in Chemical Process Simulation
… capabilities of such machines to solve the large, sparse matrices which arise from such problems. Since the row and column ordering of these matrices has a direct impact on the efficiency of frontal methods, this work has developed a number of ordering strategies specifically designed for …
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Hypergraph-Based Combinatorial Optimization of Matrix-Vector Multiplication
… multiplication for relatively small, dense matrices that arise in finite element assembly. Previous work showed that combinatorial optimization of matrix-vector multiplication can lead to faster assembly of finite element stiffness matrices by eliminating redundant operations. Based on a …
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Approximation of the Scattering Amplitude using Nonsymmetric Saddle Point Matrices
… as GLSQR or QMR. Then, we use techniques from "matrices, moments, and quadrature" to compute the scattering amplitude without solving the system directly.</p>
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Algorithmic advances in learning from large dimensional matrices and scientific data
… in machine learning and data analysis related to large dimensional matrices and scientific data. Two key research objectives connect the different parts of the thesis: (a) development of fast, efficient, and scalable algorithms for machine learning which handle large matrices and high dimensional …