Sentence examples for Properties for optimization from inspiring English sources

We establish basic properties for optimization: a local-to-global optimality criterion, the steepest descend algorithm by successive k-submodular function minimizations, the persistency property, and the proximity theorem.

The versatility in structural modification is highlighted, and that it offers an effective avenue to access chiral ligands of diverse electronic and steric properties for optimization in various catalytic asymmetric transformations.

Although these functions limit the set of metrics supported by the approach, they are appropriate for a wide range of scenarios and have mathematical properties suitable for optimization using feature models (Sanchez et al. 2013).

These effects should be taken into account when graphene is combined with silicon photonics platforms, certainly when attempting to use such platforms to determine graphene's characteristic properties and for optimization of future graphene-integrated silicon photonics devices, such as optical modulators and sensors.

In this project, we are designing algorithms with provable convergence properties for decentralized optimization -- meaning that several interconnected units act as local decision-makers and optimize local costs, coordinating with each other through constraints.

This study is valuable for improving the fundamental understanding of the relationship between pressing variables and panel properties for process optimization.

Therefore, steel suppliers who develop dual phase steels are interested in predicting the microstructure-properties relationship for optimization of microstructural design.

The fitness landscape revealed by the genetic algorithm indicates that dynamic causal modeling has excellent properties for evolution-driven optimization techniques.

We use an advanced framework known as multi-stage convex relaxation, which has good numerical properties for non-convex optimization problems [ 41].

Limitations include physically irrelevant and/or empirically based agglomeration kernels, difficulties in assessing the influence of process conditions (e.g. hydrodynamics, particulate physical properties), solution method efficiency for optimization and control applications, and loss of information on constituent particles.

A multi-property fitting algorithm developed for optimization of parameters of the new hydrate model is introduced in part II.