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(1) X 1 X 2 ⊗ Y = X 1 ⊗ Y X 2 ⊗ Y (2) X ⊗ [ Y 1 Y 2 ] = [ (X ⊗ Y 1 ) (X ⊗ Y 2 ) ] (3) (X 1 ⊗ Y 1 ) ⊕ (X 2 ⊗ Y 2 ) = [ X 1 X 2 ] ⊗ Y 1 Y 2 Under the assumption that the operations ⊗ and ⊕ between two domain elements consume Θ(1) computation time, a straightforward implementation of a matrix multiplication between two n × n matrices can be computed in Θ(n) time.
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The implementation of such a matrix is introduced to assess behavioral performance of complex products at the early design stage.
The method utilizes a numerical implementation of a transfer matrix solution to the equation of motion.
The method utilizes a numerical implementation of a transfer matrix solution to an asymptotic form of the equations of motion.
The method utilizes a numerical implementation of a transfer matrix solution to the analytical equation of motion.
The efficacy of the simulation language is demonstrated by means of continous-discrete implementation of a Dynamic Matrix Control strategy.
We describe the design and implementation of a sparse matrix infrastructure for Star-P, a parallel implementation of the Matlab® programming language.
This paper presents a case study in the implementation and use of a matrix organization.
This paper presents the implementation of a parallel sparse matrix solver for improving the computational speed of an electromagnetic transients (EMTs) simulation software.
However, the input of this implementation requires a matrix inversion step which cannot always be precomputed if the channel and noise statistics are not available [9].
SiAlO2N elastic constants C11 and C44 were extracted using a numerical implementation of the matrix method for SAWs traveling in multilayer structures, and were found to be C11 = 160 ± 30 GPa and C44 = 55 ± 5 GPa.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.
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CEO of Professional Science Editing for Scientists @ prosciediting.com