Thus, the optimization problem can be expressed as follows: Optimization problem (i) maximize g m m ∈ [ [ 0, M - 1 ] ] ∫ 0 T ln 1 - e - ∑ m = 0 M - 1 ∥ g m ∥ 2 P ∑ n ∈ Z ∑ m = 0 M - 1 | g m, n ( t ) | 2 γ dt, subject to g m m ∈ [ [ 0, M - 1 ] ] ∈ C i.
This can be expressed as an optimization problem, since the difference between the obtained throughput by simulations and the target throughput must be minimized.
Different conceptions of bounded rationality are divided as to whether bounded rational behavior is thought to be fundamentally non-optimizing or whether it can be expressed as a (constrained) optimization problem and as to whether it involves any kind of meta-reasoning (Klein [2001]).
We show that the offline throughput maximization problem can be expressed as a convex optimization problem.
Given the analysis prototype, we show that the minimization of the stopband energy of the synthesis prototype, subject to the simplified NPR constraints, can be expressed as a convex optimization problem.
The optimization in (13) can be expressed as the following linear optimization problem [25]: [ σ * b * ] = arg min [ σ - b ] b + ∑ j = 1 N σ j C ( γ j R ) (14).
The maximization problem (3), where the sample estimate R ^ is applied instead of R i+n, is mathematically equivalent to the MVDR sample matrix inversion (SMI) beamforming [20], which can be expressed as the following convex optimization problem: min w w H R ^ w subject to w H a 0 = 1 (5).
As a result the amplifier design problem can be expressed as a special form of optimization problem called geometric programming, for which very efficient global optimization methods have been developed.
For this system the set of all rate tuples that can be achieved via superposition coding and Gaussian signalling (SPCGS) can be parameterized by a set of power loads and partitions, and the boundary of this set can be expressed as the solution of an optimization problem.
Therefore, the new measurement matrix optimization can be expressed as: hat{boldsymbol{Phi}}= arg underset{boldsymbol{Phi}}{ min }{parallel boldsymbol{G}-{boldsymbol{I}}_Nparallel}_F^2 (33)where G is the Gram matrix defined as G = A H A. Thus, we propose an algorithm to decrease the mutual coherence of A which is determined by minimizing the whole elements of G except the main diagonal elements.
