Sentence examples for be solved by minimization from inspiring English sources

We obtain analytic formulae for the gradients of cost functionals with respect to the parameters of the grating profile and the thickness of the layers, so that the optimal design problems can be solved by minimization algorithms based on gradient descent.

Once the optimization problem is solved by minimization of the cost function, the displacement field is determined and the local strain fields are evaluated and plotted.

The solution to the l1-norm minimization is not as easy as the classical minimum variance LP analysis problem but it can be solved by recasting the minimization problem into a linear program [12] and then using convex optimization tools [5].

The localization problem is modeled as a sparse question and can be solved by the L1 minimization.

But when Y ∈ RM × 1 is K-sparse signal, Function (2) can be solved by the l0-norm minimization based on CS [9, 10].

The problem in Eq. 7 can easily be solved by applying l 1norm minimization algorithm such as least-angle regression (LARS) [14].

In the mixture prior, given dω and a set of prototypes, we define the posterior loss as And similarly define as the parameters that minimize this loss: This minimization problem can be solved by running P (the number of prototypes) minimization problems, one per prototype.

In this paper we show that the unconstrained continuous global minimization problem cannot be solved by any algorithm, so we consider the box constrained continuous global minimization problem.

Therefore, the minimization problem must be solved by direct search algorithms.

On the other hand, the sub-differential Eq. (4), discretized by (7), is formulated as a minimization that will be solved by the dual formulation [25].

As shown in (13), the security-constrained line loss minimization problem can be solved by finding the value of (Delta dot{I}_{U}^) of the minimum magnitude that satisfies the security constraints of the system, denoted by (Delta dot{I}_{U}^Delta I_{{U{text{x}}}}^ {text{ + j}}Delta I_{Uy}^).