Sentence examples for approximation to this problem from inspiring English sources

By using the Model Predictive Control (MPC) technique, the discrete-time approximation to this problem is converted into a nonlinear programming problem and then solved.

Sakov [1] and Ford and Smith [2] have independently resolved the "infinite forward scatter" paradox encountered in earlier applications of the Born approximation to this problem.

A rough approximation to this problem is given by the local Lax-Friedrichs (or Rusanov) method, in which the matrix (Q_{ij}) could be seen as an approximation of (vert A_{ij}vert) given by a diagonal matrix defined in terms of the largest eigenvalue of (A_{ij}) in absolute value.

Using these two criteria, we estimated the completeness of the reconstructed networks; however, it has to be kept in mind that there is no precise approach on how to estimate the completeness of genetic networks, thus our results may be used as an approximation to this problem.

Various approximations to this problem have been proposed in recent years.

A self-consistent homoenthalpic approximation to this micro macro problem is proposed, based on the assumption of a common enthalpy temperature relation for the whole casting which is used for the description of latent heat production on the macroscale.

That is why we have applied adiabatic approximation, to solve this problem.

The big approximation is your approximation to the problem you want to solve.

Kempe et al. (ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 137 146, 2003) showed that the standard greedy algorithm, which selects the node with the maximum marginal gain repeatedly, brings a e − 1 e -factor approximation solution to this problem.

Again we provide a (1− d/2 n -approximation algorithm to this problem.

The first approximation to a distributed problem in this scenario is to compute m centralized solutions, one for each node of the network, so the classical KLS problem could be written as: begin{array}{*{20}l} &underset{f_{j}inmathcal{H}_{K}}{min}sumlimits_{i=1}^{n} z_{i}-y_{i})^{2} z_{i}-y_{i}j=1}^{2}+sumlimitsVert f_{j} Vert_{mathcal{H}_{K}}^{2} end{array} (8).