At the core of the optimization is the function that computes the score of each sequence given the current value of the pair of weights.
Through optimization, the objective function is successfully improved with respect to the reference geometry.
During multiple response optimization, the desirability function approach was employed to concurrently maximize Y1 and minimize Y2.
However, in the context with reliability-based optimization, the performance function is expressed with respect to the random variables and the design variables, respectively.
The solution of the optimization function was the optimal protein path vector x → of N elements x i, representing protein i was in the lowest-cost path or not.
Digitized photos were prepared in Adobe Photoshop (version 7.0); contrast and color optimization were the functions used and affected the whole image equally.
By taking the absolute value of the optimization function, the method can be extended to detect brighter than iris, in cases of abnormal lens opacity (cataract) or the occasional red-eye effect caused by coaxial illumination.
At the same time, since the base station's transmit power, P BS, appears both in the numerator and in the denominator of the optimization objective function, the optimization objective function is not convex.
In the S-I scheme, the optimization function finds the optimal RCPC code rate R for a given channel data rate C as argmin { R } F ¯ = { R ∗ } s.t.
Clearly, when the data does not follow one or more of the hypotheses, the optimization function can overcome the prior belief with a strong support from the data.
