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The proof is based on a regularization and approximation strategy designed for our time-dependent framework.
In addition, we propose a solution approach based on an outer approximation strategy and show the algorithmic advantage of such framework for this class of programs.
It is based on a global approximation strategy, where the structure response is given by a system of neural networks trained by means of finite element analyses, and on genetic algorithms, that results particularly profitable due to the presence of integer variables.
However, the multitarget Bayes filter has no practical utility without an approximation strategy.
It is a quite remarkable that the algorithm exhibits such excellent convergence properties which illustrates that the proposed successive approximation strategy works quite well.
However, other choices have been suggested in the literature, for example, a Gaussian approximation strategy [24], which may be applied as well to the particle filter employed here.
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Approximation strategies of the obtained infinite dimensional control laws are proposed.
The use of approximation strategies is designed to reduce the number of detailed, costly computer simulations required during optimization while maintaining the pertinent features of the design problem.
Note, that the interpolation and averaging approximation strategies have the disadvantage that they are qualitatively different.
In these approximation strategies, objective function is estimated by approximation model and the optimization problem is solved utilizing the approximated values.
We propose three different approximation strategies to do this: The simplest strategy to estimate P y=1|x) is to compute a probability based on available weak classifiers.
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