Sentence examples for matrix which reduces from inspiring English sources

The matrix elements can be expressed in form of Toeplitz-plus-Hankel matrix, which reduces the computational complexity.

The final image product and the intermediate result are stored in the same matrix, which reduces the memory requirement and computational complexity.

This aggregate gradation provided a dense matrix, which reduces the amount of voids within the mix leading to higher compressive strength.

Contrarily to the methods developed in [1, 13], the proposed algorithm does not require the additive noise power estimation nor the eigenvalue decomposition of the correlation matrix, which reduces considerably the overall computational complexity.

Obtained characteristic equation is further analytically simplified using the basic properties of block matrices, and in order to solve the problem numerically, it is sufficient to consider the determinant of the 6 × 6 matrix instead of taking the determinant 12 × 12 matrix, which reduces the numerical work considerably.

Its matrix representation is the Jacobian matrix, which reduces to the gradient vector in the case of real-valued function of several variables.

These covariance matrices may equivalently be expressed as the outer product of subsource mixing matrices, which reduce to mixing vectors when the spatial covariance matrices have rank 1 [12].

The obtained criteria are in terms of linear matrix inequalities without transcendental equation, instead of nonlinear matrix inequalities, which reduces the computational burden.

Moreover, the problems of designing controllers are converted into solving optimal problems of a series of linear matrix inequalities, which reduces the computation complexity.

The function AdaptiveMatrix performs matrix adaptation which reduces H to H ′. The function BP performs BP algorithm based on H ′ and stores its returned value into the boolean variable BP result.

The proposed parity check matrix H, which reduces the non-volatile memory demand, can be represented by a j × k array of circulant permutation sub-matrices shown as follows: H  = I ( y 0, 0 ) I ( y 0, 1 ) ⋯ I ( y 0, k - 1 ) I ( y 1, 0 ) I ( y 1, 1 ) ⋯ I ( y 1, k - 1 ) ⋮ ⋮ ⋮ ⋮ I ( y j - 1, 0 ) I ( y j - 1, 1 ) ⋯ I ( y j - 1, k - 1 ) (4).