Sentence examples for practical perturbation from inspiring English sources

We present the first practical perturbation method for optimizing matrix stability using spectral abscissa minimization.

The approach taken here is first to ensure that the model used properly meets asymptotic limits (e.g., predicts equilibrium behavior for high kinetics or long contact times), and then to devise practical perturbation variables that provide quantitative engineering insight into process uncertainties.

However, in practical applications, perturbations in the input data can arise from many sources, such as roundoff errors or sensor noise.

Validation results against linear and nonlinear physical plants are convincing, with good closed-loop performance and robustness in the presence of practical constraints and perturbations.

However, this kind of approach cannot have reliable results under practical situations where perturbations are inevitable.

If the performance of a structure is too sensitive to perturbations its practical application is limited or may not be realizable in practice.

Exact preservation of these equilibria is desirable as many practical problems are small perturbations of such balance.

In this Letter we show how one can robustly and effectively perform practical computations using small perturbations to a very simple globally coupled network of oscillators.

In some practical applications, by using this perturbation series, it would become unnecessary to perform time consuming computations to get coupled eigenvalues, and hence the present results obtained by the perturbation analysis might have considerable engineering importance.

We highlight and compare the practical performance of the derived perturbation bound and condition number through a numerical example.

To investigate the extent to which data perturbation technologies are practical, we organized the Critical Assessment of Data Privacy and Protection (CADPP) Workshop as a community effort to evaluate the effectiveness of these methodologies (in particular differential privacy approaches [ 20]) for genomic data.