This leads to numerical inconsistencies about the inequality criteria of the linear program (i.e., B R x≥0) and can be solved by replacing the inequality constraint with B R x≥ε, where ε is the minimum positive number available in MATLAB (machine epsilon).
Although most results from the programs were identical, we did find some minor numerical inconsistencies.
Numerical results demonstrated inconsistencies and overall conservatism inherent in the design equations and the possible sources of this conservatism were also identified and discussed.
Compared with existing MYS models, the newly developed MMYS model eliminates the main sources of numerical errors caused by inconsistencies between the model and discretized backbone curve, which significantly improves the numerical stability and convergence rate of the Newton Raphson (N R) iterative process at a structural level.
The time-stepping at the sub-domain boundaries is done using a DuFort–Frankel scheme, which leads to a time step limit given not by instabilities, but by the damping rate of numerical oscillations driven by inconsistencies in the formulation of initial conditions or the temporal variations in the true physical solution.
In particular, this model is not based on Euclidean space and not on an infinite-precision arithmetic which lead to lacking numerical robustness and to topological inconsistency of implementations on a computer; it rests on a finite, discrete geometric domain called grid partition which takes into account finite-precision number systems available in computers.
Potential explanations for this inconsistency are numerous (e.g. data entry or coding errors; rounding up or down of numerical values; replicated values during the same treatment period; different error deviations of different haematology analysers or blood pressure-measuring devices).
We will clarify possible inconsistencies induced by independent numerical approximations of the unperturbed system and the LDP, based on which we propose to define both the dynamic solver and the MAM on the same approximation space for spatial discretization.
There is some arbitrariness to which glyphs are grouped together, and there are inconsistencies in the assignments of numerical codes and the use of affixes which make the system rather complex.
Given that the physical sub-grid models and computational grids are the same in all the simulations, the inconsistency between results points to a significant impact of numerical methods and, hence, numerical error.
