Sentence examples for matrix implementation and from inspiring English sources
Privacy amplification can be very memory efficient when using a linear-feedback shift-register-based matrix implementation and only requires memory equal to the sifted key length (i.e., (m_{mathit {key}}) bits).
The TLISMNI method has desirable features that include avoiding numerical differentiation of the forces, allowing for an efficient sparse matrix implementation, and ensuring that the kinematic constraint equations are satisfied at the position, velocity and acceleration levels.
Using the TLISMNI method, which does not require numerical differentiation of the forces and allows for an efficient sparse matrix implementation, for solving complex and stiff structure problems leads to significant computational cost saving as demonstrated in this paper.
A simple matrix implementation for LM formation and a MATLAB based stable model extraction approach is proposed.
Measurements on different systems and comparisons with GotoBLAS, Intel Math Kernel Library (IMKL), and AMD Core Math Library AMCLL) show that the matrix implementation presented has a very high efficiency.
Taking the advantage of the symmetry makes the covariance matrix implementation nearly twice faster.
In most cases, reconstruction results are not significantly impacted by the specific choice of system matrix implementation [30].
This allows to work with discrete integral operators, i.e. matrices, in the implementation and leads to some perturbed Galerkin formulation given in Section 2.3.
The paper defends the choice of the control configuration and argues that when using MIMO design procedures, enforcing some structure upon the transfer function matrix makes the controller implementation and fine-tuning easier.
1) Computational complexity: Regarding the computational complexity, we compare the major multiplications involved in statistical matrix construction, EVD or SVD implementation, and MUSIC spectrum search.
However, SLT without the in-place algorithm is not suitable for the parallel implementation, and the matrix formation and algorithm design of SLT lack universality, either.
