Sentence examples for optimization under the from inspiring English sources

Even in the case of Chebyshev optimization, auditory warping appears to improve the numerical conditioning of the filter design problem; this applies also to optimization under the Hankel norm which includes an optimal Chebyshev design internally as an intermediate step.

Recently, Bhatia [13] established necessary and sufficient optimality conditions for strict efficiency of higher-order in multi-objective optimization under the basic regularity condition and generalized higher-order strong convexity assumption, respectively.

It is shown that the presented two-stage multi-fidelity model is more appropriate than any single-fidelity counterpart in the honeycomb optimization under the same computational cost.

The results compare favorably to other algorithms such as Genetic Algorithm, Population Based Incremental Learning, as well as Particle Swarm Optimization under the same conditions of simulation.

This approach provided statistically significant quadratic model, which was adequate to predict response and to carry out optimization under the conditions studied.

Further, the initial flight time and the time of flight are taken as design variables for parameter optimization under the objective of minimum fuel consuming.

Starting from the problem of designing the optimal polarizations for the scalar measurement system with joint Tx/Rx polarization optimization, under considering the minimum MSE of the estimator as the figure of merit, a sequential method of selecting the optimal Tx/Rx polarizations for such system is developed by owing to a suitable reformulation of the considered non-convex design problem.

The parallel cat swarm optimization (PCSO) method is an optimization algorithm designed to solve numerical optimization problems under the conditions of a small population size and a few iteration numbers.

This paper provides an overview of the key contributions and recent advances in the field of process optimization under uncertainty over the past ten years and discusses their advantages and limitations thoroughly.

We can observe that when is small, that means the effect of the estimation error is less dominant than the one of the background noise, the optimization under partial CSI is closed to the one under perfect CDIT.

His research focuses on the development of new methodologies for large-scale dynamic optimization under uncertainty, and the application of these methodologies to the design of practical revenue management strategies across various industries ranging from airlines and retail to online advertising.