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Now we introduce the Wiener integral with respect to a one-dimensional fBm (beta^{H}).
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Due to the low-rank structure the total complexity of the algorithm depends linearly with respect to the one-dimensional grid size.
However, most traditional MU-MIMO systems are designed with respect to a two-dimensional (2D) world.
The method was illustrated with respect to one-dimensional processes and required the assumption of stationarity.
Now we aim at introducing the Wiener integral with respect to one-dimensional sub-fBm (S^{H}).
Since the optimization is with respect to a time function, this is an in finite dimensional problem.
On the other hand, an expectation with respect to one dimensional marginal of a diffusion process is in most cases numerically tractable.
This is particularly evident by comparing the structural and spectroscopic data predicted for the α and β crystals with respect to the respective one dimensional infinite chain models.
We can easily become numb to a one-dimensional character with predictable antics: One more rape?
Maximize T in (A.1) with respect to θ that is on the one-dimensional straight line determined by the initial parameter vector θ0 and the gradient vector u (Linear Search; e.g., Kowalik and Osborne, 1968).
In this regard, there is a specificity of random motions in a three-dimensional medium with respect to the one- and bi-dimensional cases, for which the exploration is always compact since the fractal dimension D f (less or equal to 1 and 2, respectively) is necessarily smaller than D w.
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