Sentence examples for matrix multiplication with from inspiring English sources

Such system can be easily used to accelerate matrix multiplication with virtually any matrix sizes.

However, the matrix multiplication with the ⊙ term in (3.4), (3.6) will no longer be dimensionally correct, so a new formula is needed.

In order to merge the different sampling rate domains, the input (mathrm {x}_{k-N-L}^{k}) is M-fold upsampled by matrix multiplication with U N) and the filtered noise is D-fold decimated by the matrix multiplication with D(N).

In the sensing operation, K-sparse signal vector x, i.e., n-dimensional vector having at most K non-zero elements, is transformed into m-dimensional signal (measurements) y via a matrix multiplication with Φ.

MCM is a special case of vector matrix multiplication with N i  = 1, so if N A,MCM adders are needed for an optimal MCM adder graph with N unique coefficients, N A,SOP = N A,MCM + N - 1 adders are needed for the corresponding optimal SOP.

Hence, these alternative algorithms are unfavorable for matrices of practical sizes. 2 Intuitively, one circumvents the expensive matrix-matrix multiplication with a domino-like chain of 2J−1 (less expensive) matrix-vector multiplications per transmitted symbol vector.

(a) Subblock matrix multiplication scheme with timing information; (b) algorithm mapping and data sharing on SmartCell system.

1 Matrix multiplication combined with matrix inversion can be implemented using the Strassen's algorithm in [40] and the improved Coppersmith-Winograd algorithm in [41].

(1) Calculate a covariance matrix, Z, from the square of the distance matrix, by multiplication with centring matrices, producing a centred matrix where, I is an identity matrix of size, and L is a square matrix of ones of size n.

The next step is to find consistency matrix, the matrix consistency is obtained from pairwise comparison matrix multiplication Table 1 with average priority weight in Table 3. Consistency matrix is shown in Table 4. Table 4 Matrix consistency table ax 2.979 1.720 0.967 0.532 0.292 0.169.

For each state estimate, in (4), matrix inversion is computed with asymptotic complexity O ( R 3 ) [58], where R is the dimension of measurement noise covariance R or the number of available measurements; in (6), matrix multiplication is computed with asymptotic complexity O ( P 3 ) [58], where P is the dimension of error covariance or the dimension of the state vector.