Considering the variation in the minima of the objective function f 1 x), it can be seen that where the number of MDCs is greater, the total moving distance is also greater.
This corresponds to a decrease of ≥ 6.635 (one parameter difference) in the minimum objective function (-2 × logarithm of the likelihood of the results), as the difference in objective function between hierarchical models is approximately χdistributed.
For nested models, a change in the minimum value of the objective function of ≤10.83 (α = 0.001, degree of freedom = 1) was used to define statistical significance for inclusion of a parameter to the model using a likelihood ratio test.
As already discussed, local nonlinear least squares (NLS) algorithms will find the local minima of the objective function in the vicinity of the initial point.
The α level of 0.01 corresponds to a reduction of 6.64 (χ, p < 0.01; 1 degree of freedom) in the minimum objective function when 1 parameter is added to the model and was used to examine significance.
A model parameter or a covariate was retained in the model when including this parameter in the model resulted in a decrease of 6.63 points (χ distribution, 1 degree of freedom, p = 0.01) in the minimum value of the objective function (ΔMVOF ≥ 6.63) or vice versa with backward deletion from the model.
The solution methodology was based on a characterization of Nash equilibrium in terms of minima of the objective function and relied on the GA, SA and HSAGA approaches to find these minima.
In this example, data noise did not affect the computational cost in obtaining the (global) minimum of the objective functions.
For us, our emigrants represent help in development that Europeans do not give us (few countries reach the minimum objective of 0.7% of GDP in development assistance).
Calculate the objective function value, and select a minimum of objective function as producer in the initial populations.
