Sentence examples for minimise complexity from inspiring English sources

The mapping of Q ~ ε to an allowed group dimension, jointly with the setting of ε, permits the implementation of different reconfiguration strategies, e.g., Maximise performance : Q = arg min Q ̂ ∈ Q { Q ̂ ≥ Q ~ ε } Minimise complexity : Q = arg min Q ̂ ∈ Q { | Q ̂ − Q ~ ε | }. (60).

Safe systems typically minimise complexity by emphasising modularity in design, which by its nature constrains any interactions between components to within their own local module.

Rather than accrete increasingly complex IT in our workplaces that impede cognition and decision-making, we probably need to actively consider information apoptosis as an essential strategy to minimise complexity.

In the spirit of making our model as simple as possible, but not any simpler, the choice of nodes and interactions was made so as to minimise the complexity without losing essential information.

We accept the criticism of some authors who state that measures of dental anxiety that are restricted to a single dimension tend to minimise the complexity of the multifactorial phenomena that characterises the dental anxious individual [ 14, 52].

Other research suggests that decision-support tools, and by extension research tools, may be more effective where they minimise 'cognitive complexity' for the decision-maker (see Tompkins et al. 2008).

By exploiting the concept of morphing, designers may devise adaptable structures, capable of accommodating a wide range of service conditions, minimising design complexity and cost.

For maintaining clarity and minimising the complexity of the ensemble, we use the condensed version described in Tyson et al. 2011 [ 49].

It was cheering to hear him demand a a clear commitment to minimise bureaucracy and complexity.

The two previous equations may be simultaneously implemented in order to minimise the estimator complexity, thus the final CIR estimate presents non-null elements and zeros in the remaining [26].

Understanding the role of the nonlinearity in the model enables us to minimise unnecessary model complexity, which decreases both the sensitivity of the model and the number of parameters.