Sentence examples for problems can be derived from inspiring English sources
Since the characterizations of nonemptiness and boundedness of the solution set for strong vector equilibrium problems can be derived when F is a single-valued map, it is natural to ask whether characterizations on nonemptiness and boundedness of the solution set for (SVSEP) can be obtained in the case that F is multi-valued, which constitutes the motivation of this article.
Simplificat ions of the design problem can be derived if the control law is restricted to be of proportional-integral character.
An optimal control policy of a dual adaptive control problem can be derived by solving a stochastic dynamic programming problem, which is computationally intractable using conventional solution methods that involve sampling of a complete hyperstate space.
The approach is based on heuristic rules that assist the formulation of the real world design problem in such a way that a mixed integer nonlinear programming problem can be derived.
When we assume that the behaviour of the modelled system is 1-periodic, a linear programming problem can be derived from the discrete implicit system, to answer both, the minimum (maximum) cycle time and the optimal initial sojourn time of tokens.
The continuity of the transverse shear stress across the interfaces is specified according to Hooke's law, and subsequently the equations of motion of this higher order problem can be derived in analogy to a homogeneous single-layer shear deformable shallow shell.
The solution for the CF power allocation problem can be derived equivalently to the AF case.
From Eq. 30, an eigenvalue problem can be derived by assuming that θs are harmonic functions of τ expressed as theta ={e}^{jomega tau}varTheta (34).
By this way, the Green function of problem for the domain in the jump problem can be derived from the Green function of problem for some simple canonical domain by conformal mapping.
The solution of this problem can be derived by merging both conditions and related matrix operations, and results in the following vector of CCs, namely: s c = - P CC μ I + P CC 0 s d = W s d, (12).
The crux of the quantum adiabatic algorithm which rests on the adiabatic theorem lies in the possibility of encoding a specific instance of a given decision problem in a certain Hamiltonian (this can be done by capitalizing on the well-known fact that any decision problem can be derived from an optimization problem by incorporating into it a numerical bound as an additional parameter).
