Sentence examples for minimising an objective from inspiring English sources

This type of problem can be solved using an optimisation framework, where the boundary is obtained by minimising an objective function J: Figure 8 An m dimensional dataset D is partitioned into ℋ and ℋ ̄.

The optimal integrated DER system is obtained by minimising an objective function that contains investment, operational, maintenance and environmental costs.

Subsequently, an optimisation procedure is applied to find the combination of gates and the parameters that best explain the data, by minimising an objective function that quantifies the difference between data and simulation, while penalising model size.

A risk-based approach has been incorporated in the procedure in order to minimise an objective function that involves the mitigation actions of risks.

A widely used method to minimise an objective function is the non-linear least squares analysis (see [34] for an overview on different methods).

The operating parameters can be determined by means both of a thermodynamic and of a thermoeconomic analysis, minimising a suitable objective function by analytical or numerical mathematical methods.

When dealing with mixture of GLDs, it is harder to fit a distribution to censored data using direct MLE or PWMs, since this requires maximising or minimising a much more complex objective function which can be difficult in practice.

The clustering algorithm is an iterative process for minimising the objective function involving selecting an initial random cluster membership matrix, where the sum of utilities is unity.

But he says the British are "minimising the objective damage" they are able to inflict.Given the military stalemate, the British strategy now focuses on developing Afghan forces and government structures with a view to an eventual political solution.

We fit the P-spline model by minimising the objective function: sum_{d} left y(d) - B(d)^{T} beta right)^{2} + lambda left(Delta^{2} betaright)^{T}left(Delta^{2} betaright) where Δ indicates a first difference.

NCA mainly learns the feature weights with the aim of minimising the objective function that measures the mean leave-one-out regression or classification loss over the given training data set (Goldberger et al. 2005; Yang et al. 2012).