Despite its high sensitivity, dispersion power penalty is minimized by setting a 400 GHz channel plan in O-band whose center is slightly red-shifted from the zero-dispersion wavelength of the fiber.
Alternatively, when is large, the latter term dominates and the function is minimized by setting.
which means the cost function in Eq. (7) is minimized by setting higher weight ω ′ i to lower sample cost (phantom {dot {i}!}L({{x}_{{delta }_{i}}})) using our LpcSVM algorithm.
In this interval, the probability that the cost exceeds T is minimized by setting x to T – cc (Fig. 4).
Given H T, M is minimized by setting p − 1 = (1 + 2 H T − 1 ) / 2, p − 2 = (1 − 2 H T − 1 ) / 2, and p − i = 0 for all i ≥ 2. If these mean frequencies are distributed between the two populations such that (p 11, p 12, p 21, p 22 ) = (1, 0, 2 H T − 1, 1 − 2 H T − 1 ) or (2 H T − 1, 1 − 2 H T − 1, 1, 0 ), then the upper bound on F is achieved.
The reason being that, as later detailed in Section 4, the degradation of the iterative SPA due to short-length cycles in the underlying factor graph can be minimized by properly setting these switches.
From Eqs. (2) and (3) the Sum of Violation percentages (SV) over all applications can be minimized by simply setting P e n a to V a for each application a. Therefore the SLO objective can be modified to: textit{min} ; SV = sum limits_{a=1}^{A} V_{a} (7).
Downward network traffic can be minimized by servicing set of users at network edge instead of service provider's data centers (e.g., multimedia and shared data) Content Delivery Networks (CDNs), and upward traffic can be minimized by processing and filtering raw data (e.g., sensors monitored data) and uploading the processed information to cloud.
