The sensitivity analysis is performed for the general objective function formulated as a function of the eigenmode in continuum form.
In fact, they established the R-linear convergence of the spectral gradient method for strongly convex quadratics of any number of dimensions, and they also proved the locally R-linear convergence for the general objective function.
Several spectrum access and sharing schemes have been proposed for CRNs with the general objective of maximizing the CRN goodput without violating the interference (and consequently, outage) constraints of the primary licensed networks [3, 4, 9 18].
A general objective for this study was to obtain a comprehensive set of threshold sensitivity and temporal-processing measures in hearing, vision, and touch, using identical psychophysical procedures and similar stimuli for each sense.
With the introduction of increasingly powerful mass spectrometry (MS) techniques for clinical research, several recent large-scale MS proteomics studies have sought to characterize the entire human plasma proteome with a general objective for identifying thousands of proteins leaked from tissues in the circulating blood.
Then, the general objective function for this problem is (25) begin{aligned} text{min} & left | tilde{mathbf{p}}_{1}^{circ}(hat{theta})right |_{1} text{subject~~to} & left | {mathbf{Z}}_{1} - {boldsymbol{Phi}}_{1} (f_{c}, hat{theta}) tilde{mathbf{p}}_{1}(hat{theta})right |_{2} leq epsilon end{aligned} (25).
The general objective function for this problem is (27) begin{aligned} text{min} & left | bar{mathbf{p}}_{1}(hat{theta})right |_{1} text{subject~~to} & left | {mathbf{Z}}_{1} - {boldsymbol{Psi}}_{1} (f_{c}, hat{theta}) bar{mathbf{p}}_{1}(hat{theta})right |_{2} leq epsilon end{aligned} (27).
In the penalized logistic regression, the general objective function for maximum likelihood estimation can be written as L penalized = L − λf β where the penalty component f is a function of the regression coefficients and λ is the sparseness control parameter.
As an example of a by-product of Theorem 25, we obtain a generalization of known results that could be applied to finding stationary points of the Kuhn-Tucker type for non-smooth programming problems with general objective functions.
The general objective of our studies is to generate preclinical evidence supporting the decision process for further vaccine development in children and infants.
