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Next, we shall establish the geometrical convergence of these sequences.
end{aligned} We give a geometrical convergence theorem (see [8]).
The analysis of geometrical convergence of the Schwarz algorithm has been proven in [8, 16, 17].
This approach combines the geometrical convergence of solutions and the uniform convergence of variational inequalities.
In Section 2, we construct a monotone iterative scheme and establish its geometrical convergence to the solution of VI (1.4).
In order to formulate the theorem, the notion of geometrical convergence of a t-norm is defined.
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The approach here proposed enjoys many advantages over standard techniques, such as considerably higher convergence rates, geometrical flexibility, and, in our experience, the possibility of using elements with large aspect ratios without significant deterioration in accuracy.
The energy convergence criterion for geometrical optimization was chosen as 10−4 eV, while the energy gradient and atomic offset were set at 0.1 eV/Å and 0.005 Å, respectively.
The velocity of this convergence is not necessarily geometrical.
We first perform the accuracy check of the geometrical quantities of the interface, and the convergence check for different stiffness numbers as well as fluid variables.
This method eliminates the geometrical errors in the experimental setup because convergence will occur, regardless of the detector distance or angle.
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