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Let stand for a matrix multiplication in GF 5); (4).

Then, the matrix multiplication in (14) has a complexity of.

Computing the matrix multiplication in (19) requires a complexity around the order of.

For R-S codes, the matrix multiplication in (1) uses GF arithmetics.

Then, performing the matrix multiplication in (A.6) for the first row of the matrix will give (A.7).

Because the noise subspace in (25) is fixed, we consider the computational complexity of matrix multiplication in one data segment.

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To give an answer to the key question: whether the increasing order convergence is worth in view of increasing the matrix multiplications in each iteration, it is requisite to incorporate the notion of efficiency index, (p^{1/theta}), whereas p and θ stand for the rate of convergence and the computational cots per cycle, respectively.

This became possible by replacing the inverse of a matrix-matrix multiplication in the RZF with a sum of weighted matrix powers.

There is a set of work that proposes to compress row-starts, such as DCSR [4] and Coarse index + Skip list [6], but they involve non-negligible performance overhead as we describe below: DCSR [4] was proposed to improve the efficiency of matrix-matrix multiplication in a distributed-memory environment.

We adapt the problem to the VMT framework defined in [ 11], which incorporates efficient matrix multiplication subroutines in order to accelerate standard dynamic programming algorithms.

This is the function of matrix multiplication Pc in (26a).

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