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Finally we apply our theory to approximation of solution of using iterative methods (fixed point iteration methods).
The second part is devoted to the modified iteration methods and the integration of sensor signals into the iterative computations.
The iteration methods we use are preconditioned GMRES methods.
We propose two iteration methods to do such a job.
We also compare the rate of convergence between iteration methods.
Iteration methods and Maple programming are used to determine solutions.
Iteration methods are also applied for solving optimization problems.
In fact, all hybrid iteration methods can be replaced (or modified) by monotone hybrid iteration methods, respectively.
Numerical results showed that the MMS iteration methods are more efficient than the well-known Newton method and the classical projection fixed-point iteration methods [1].
Traditional iteration methods struggle for the convergence difficulty for such kind of constitutive model.
However, two iteration methods might exhibit different behavior in the coordinate-transformed finite-difference model (FDM).
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