A close approximation to maximizing the minimum SNR for ZF receiver is also provided under the assumption of M<N T. These two error rate-based percoding matrix selection criteria (SC) are summarized as follows.
In particular, we optimize the hydraulic fracturing design by implementing a variant of the Simultaneous Perturbation Stochastic Approximation algorithm to maximize the net present value of the shale asset, including the cost of fracturing and the revenue from gas production.
In this section, we introduce an approximation algorithm called SPCPM to maximize the energy preservation of a sector-shaped wireless sensor network.
Two relay designs which comply with the above design choices are a heuristic approach RRANOMAX in [12] and a novel iterative scheme herein developed which seeks to maximize an approximation to the sum mutual information.
In the above equation, we try to maximize the approximation performance of y i (i = {1,2,…,N|x i (j) ≠ 0}) by x i (j d j, i.e., by the target atom d j and its corresponding representation coefficient x i (j).
Applying the asymptotic formula, we provide an approximation of the optimal investment strategy to maximize the expected terminal wealth subject to a risk constraint on the Value-at-Risk, which is defined with respect to finite-time discounted net loss.
Now, we intend to maximize the sum-rate approximation ∑ k R k with respect to w under the total transmit power constraint.
More specifically, the authors propose an approximation technique for selecting an optimal path set to maximize the probability of successful transmission in cases where paths may not be independent and their failure probability can vary as well.
All chains used the Brownian motion approximation as the mutation model and relied on adaptive heating to maximize the visited space (default parameters).
Another important result that follows from the analytical approximation is that early stopping must be always used in order to maximize the classifier performance, even in the case of infinitely "soft" learning, since after large number of training iterations (N → ∞) only the cells with the maximum λ i survive.
