matrix multiplication

The group multiplication of GL(n, R) is the usual matrix multiplication, the inverse map takes a matrix A on GL(n, R) to its inverse [A.sup.-1], and the identity element is the identity matrix I.

The Schulz-type iterations are strongly numerically stable, that is, they have the self-correcting characteristic and are essentially based upon matrix multiplication per an iterative step.

We present a linear layout of computational cores for sparse matrix multiplication, which can effectively reduce the average memory access time.

Current generation DNNs, such as AlexNet and VGG, rely on dense floating-point matrix multiplication (GEMM) which maps well to the capabilities of GPUs, with their regular parallelism and high Tflops.

The users choose a key matrix pair [P.sub.1], [P.sub.2] and encrypt the speech signal via matrix multiplication C = [p.sub.1] * M * [P.sup.-1.sub.2].

So H[r,r] are real numbers because of phase offset after matrix multiplication. The non-diagonal elements of the effective channel matrix are

Other areas include neural networks [25], matrix multiplication [26], digital filters [27], [28], communication systems [29] and more.

* Expansion which is just normal matrix multiplication. It consists on putting the stochastic matrix P power n: the probability of a random walk from node i to node j in n steps.

For any a(x) [member of] [L.sub.[infinity]] (R), the Toeplitz operator [T.sub.n,a] acting on [F.sup.2.sub.n] (C) is unitary equivalent to the matrix multiplication operator [mathematical expression not reproducible], where the matrix-valued function [[gamma].sup.n,a] = ([[gamma].sup.n,a].sub.ij]is given by