Sentence examples for computationally relevant from inspiring English sources

Computationally relevant generalized derivatives: theory, evaluation and applications.

Local sensitivity information is obtained for KKT points of parametric NLPs that may exhibit active set changes under parametric perturbations; under appropriate regularity conditions, computationally relevant generalized derivatives of primal and dual variable solutions of parametric NLPs are calculated.

A computationally relevant theory of nonsmooth DAEs (i.e. well-posedness and sensitivity analysis) has recently been established (Stechlinski and Barton, 2016a, 2017) which is suitable for numerical implementations that scale efficiently for large-scale dynamic optimization problems.

Recently it has been observed that, for a number of operational problems, such hybrid continuous/discrete behavior can be accurately modeled using a nonsmooth differential-algebraic equations (DAEs) framework, now possessing a foundational well-posedness theory and a computationally relevant sensitivity theory.

To a first approximation, then, modules of molecular interaction are computationally relevant units of functional phenotype.

Going forward, it is critical that the field discovers which of these circuit details are computationally relevant and which are not.

In this talk, I will explore the benefits and challenges of applying such a descriptive approach to making computationally-relevant decisions regarding: (i) selecting security prompts for an online system; (ii) determining which features to include in a classifier for jail sentencing; (iii) defining standards for ethical virtual reality content.

The LANIP approach is mathematically, computationally, and practically relevant and is particularly connected to several human visual laws and characteristics such as: intensity range inversion, saturation characteristic, Weber's and Fechner's laws, psychophysical contrast, spatial adaptivity, multiscale adaptivity, morphological symmetry property.

Pairwise comparison of time series data for both local and time-lagged relationships is a computationally challenging problem relevant to many fields of inquiry.

Though clearly relevant, computationally accurate methods based in unstructured databases require a very large number of experimental samples and eat up large computational capabilities, since most of these accurate methods rely in some form of higher statistical inference such as machine learning or Bayesian approaches.

Our membership is your resource for developing cutting-edge methods to tackle the most relevant and computationally challenging problems we face today.