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To make the convergence faster, we modify these operators so that they reproduce constant as well as linear functions.
The reduction in dimension and no involvement of equality constraints will in practice make the convergence faster and easier, and a much smaller computational cost is expected.
Also the fact that fewer equality constraints are involved in solving the formulated nonlinear programming will make the convergence a lot easier.
To make the convergence faster, King[19] proposed an approach to modify the classical Bernstein polynomials so that this sequence preserves two test functions e0 and e2.
When designing an associative memory neural network, we should make the convergence speed as high as possible to ensure the quick convergence of the network operation.
A detailed performance analysis has been conducted on five commonly used multi-objective problems, each one involving distinct features that can make the convergence difficult toward the Pareto-optimal front.
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We have made the convergence test with a still greater number of basis functions.
The accuracy of this element makes the convergence rate better than the conventional cubic element or other used elements.
Furthermore, another fault tolerant coordination scheme is constructed by using uniform quantizers which makes the convergence set to go to a bounded set.
This makes the convergence very rapid regardless of the high dimensionality of θ if the Gaussian approximations for the posterior function are adequate.
Forbidding players to sever their links, although limits the applicability of model, makes the convergence of equilibrium networks possible for the network formation.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com