Sentence examples for minimum function optimization from inspiring English sources

Figures 1 and 2 are sample plots of two maximum and minimum function optimization results.

Our independent assessment of the parameters with data not used for cost function optimization allowed us to further restrict the minima and reach a compromise between cost function optimality and biological plausibility.

In this paper, a multimodal function optimization problem consisting of multiple maximums and multiple minimums is solved using an improved particle swarm optimization (PSO) algorithm.

3) Objective function optimization.

PSO has been used to solve several types of optimization problems [ 15], including function optimization and parameter optimization [ 16] and shows promise for nonlinear function optimization [ 17– 22].

A further limitation of ANNs is that, due to local minima in the cost function, optimizations starting from different initial parameters, often ends up at different minima.

Surprisingly, an individual algorithm that renders a model worse in isolation could still be necessary to produce the best overall model, suggesting that model distortion allows escape from local minima of optimization target function, here shown to be a hallmark limitation of the traditional approach.

Our hypothesis is that either the cost function optimized by NMF is not optimal for reverberation or that the optimization algorithm gets easily stuck in local minima.

As the cost function of optimization, the minimum of sensitivity and specificity was used under the assumption that both factors are equally important, as in the literature.

The objective function of optimization is set as minimum costs to consider hydrogen saving and penalty simultaneously.

Moreover, we prove that the minimum of the optimization problem can be approximated by smooth functions.