Abstract: The matrix eigenvalue inverse problem is the problem of inversely determining the matrix by using the information of the known eigenvalues and eigenvectors and other constraints. The matrix ...

Dozens of machine learning algorithms require computing the inverse of a matrix. Computing a matrix inverse is conceptually easy, but implementation is one of the most difficult tasks in numerical ...

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Abstract: The recently developed two-directional unconditionally stable single-field (US-SF) finite-difference time-domain (FDTD) method is generalized to a 3-D. The method is based on the application ...

Parallel computing continues to advance, addressing the demands of high-performance tasks such as deep learning, scientific simulations, and data-intensive computations. A fundamental operation within ...

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