Mathematically, the optimization objective (3), and constraints (4), (5), (8) can be modeled as the following mixed-integer nonlinear programming: ( ρ, p ) = arg max R subject to : C 1 : p k, m i ≥ 0, ∀ k, m, i C 2 : ∑ k = 1 K ∑ m = 1 N i p k, m i ≤ P total i, ∀ i C 3 : ∑ k = 1 K ρ k, n = 1, ρ k, n ∈ { 0, 1 }, ∀ n C 4 : F k max − F k min ≤ F k lim, ∀ k C 5 : Φ = 1 (9).
Our aim is to develop joint transceiver optimization algorithms for minimizing the worst-user MSE (min-max MSE 1 subject to the source and relay power constraints.
In this paper, we consider the differential system (1) with min-max terms subject to the initial value conditions (u(0)=u_{0}), (v(0)=v_{0}), where (I=[0,1]) is an interval, (F,G in C(I times Rtimes R, R)).
Thus, the PSVD-ATP is max information rate subject to IAP in (12), i.e., PMMI-IAP, with L k = P/2, ∀k.
Defining the set of adjustable switching thresholds as Θ = θ l | l = 1,2,…,Lc, the optimization problem can be formulated as Θ o = arg max Θ η, subject to P e, a vg ≤ δ 0. (12).
Optimization problem for production maximization is: max: : f(S,I,P,N) :subject: : to:: m.
Based on the formulations for ICU and CC optimization, the MOCO problem, which is an integration of both, can be described as maximizing both ICU and CC fitness, i.e. max (ΨICU ΨCC), subject to the constraints that both the codon and codon pair sequences can be translated into the target protein sequence.
The DST problem can be then generalized as underset{d(t)}{ max }C t) (4 which is subject to quality requirement.
Assuming that the minimum SNR required by the two transceivers is t, the optimization problem can be formulated as max w t, (38a) subject to ( SNR 1 ) lower ≥ t, ( SNR 2 ) lower ≥ t, P r ≤ P. (38b).
