On Moore-Penrose Pseudoinverse Computation for Stiffness Matrices Resulting from Higher Order Approximation
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Computing the pseudoinverse of a matrix is an essential component of many computational methods. It arises in statistics, graphics, robotics, numerical modeling, and many more areas. Therefore, it is desirable to select reliable algorithms that can perform this operation efficiently and robustly. A demanding benchmark test for the pseudoinverse computation was introduced. The stiffness matrices for higher order approximation turned out to be such tough problems and therefore can serve as good benchmarks for algorithms of the pseudoinverse computation. It was found out that only one algorithm, out of five known from literature, enabled us to obtain acceptable results for the pseudoinverse of the proposed benchmark test.
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Keyword(s):
Learning Graph Convolutional Network for Skeleton-Based Human Action Recognition by Neural Searching
2020 ◽
Vol 34
(03)
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pp. 2669-2676
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1983 ◽
Vol 66
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pp. 139-150
1975 ◽
Vol 69
(1)
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pp. 191-195
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2009 ◽
Vol 2009
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pp. 1-20
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