Sentence examples for get the approximation from inspiring English sources

We use Conditional Value at Risk (CVaR) to approximate the chance constraint, and we get the approximation problem of the chance constrained stochastic programming by using the sample average approximation (SAA) method.

end{aligned} (26)We solve this problem using the Legendre Galerkin method to get the approximation (e_{n,N}(x)).

Combining Lemmas 2.1 and 2.2, we get the approximation of v n,δ to v δ as δ δ being fixed and n→∞.

Inspired by Majumder et al. [15], it is easy to get the approximation ratio of Algorithm 2. Due to the limited space, the details of the approximation ratio proof are omitted in this paper.

When the infinite sum is truncated to N terms, we get the approximation f ( k ) ( x ) ≅ ∑ r = 1 N a r ( k ) F r ( x ) = F ( x ) A ( k ), k = 0, 1, …, n, (2.9).

By (4.23), we get the approximation solution of the first order to the stationary pattern by weakly nonlinear analysis to read {mathbf {w}}=0.4392varepsilon{boldsymbol{rho}}cos 0.5x cos (y)+Obigl( varepsilon^{2}bigr), which shows a good qualitative agreement with the numerical solution of system (1.3).

Thus, as before, we get the approximations (7), (8), (9) and (10) at the cell boundaries, and where now the numerical derivatives dependent on (14).

we then get the approximations of (12) and (13) by using the inequations in (14) and (15) R 1, tra m ≈ 1 2 min log 1 + P D g P D d 2 B 1 − α + N 0, × log 1 + P B g P B d B 1 2 − α + N 0 (16).

In this section, we will propose an effective algorithm for getting the approximation solution to problem (P), and then analyze its computational complexity.

Now, replacing Equation 37 in 30, we get the LS approximation to α δ u as (38).

For Eq. (3.1), we use the notations (2.21 - 2.23) together with the inner product given in (3.5) [1] to get the following approximation formulas: (3.8).