Sentence examples for usual optimization from inspiring English sources

Among the usual optimization algorithms, such as the direct decent method and conjugate gradient method, the convergence rate of Newton iterations near the solution is the fastest (see [15]).

Given y and ΦbigR, and assuming x is sparse in some basis Ψ (such as the 2D wavelet domain), we can solve the usual optimization problem as stated in (1) (or (2) if the measurements are noisy).

Although this alternative formulation has a relatively larger size, it eliminates the nonlinear structure in bipolar max-(T_{L}) equations and allows us to tackle the associated optimization problem by the usual optimization techniques.

Based on the theory of chance constrained programming (CCP), the UCRC, a probabilistic constraint is changed into a determinate constraint, and then the presented formulation can be solved by usual optimization algorithms.

By considering that usual structural optimization criteria based on the Von-Mises equivalent stress are strictly applicable to static loading, and that their application to fatigue loading is questionable, particularly when the applied loads are out-of-phase, an approach based on Computer Aided Optimization proposed by Mattheck in the '90s of the last century is proposed.

Google is doing the usual European tax optimization scheme.

A typical PCR setup is explained in detail and the recommended starting concentrations of the components, as well as usual ranges for optimization are tabulated.

One way to transform the lower level problem ( P x ) into an usual one level optimization problem is the so-called scalarization technique, which consists of solving the following further parameterized problem: min y λ T D y, s. t.

Note that our goal is to reach an arbitrary element in ({mathcal{A}}), although an optimal solution is, in general, an isolated point in the state space in the usual definition of optimization problems.

We firstly by using scalarization technique transform the problems (1.3) and (1.4) into a usual one-level optimization problem, which consists of solving the following parametric problem: min z f ( x, y, z ) = 〈 y, f ( x, z ) 〉 s.t.  g ( x, z ) ≤ 0, (3.1).

This framework markedly differs from usual multi-objective optimization approaches (Chung and Lee, 2012; Raab et al., 2010; Racle et al., 2012), which operate to optimize a single continuous and integrated performance score rather than explicitly target different regions of the parameter space.