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By solving the governing equation based on linearization of nonlinear theory of elasticity, two typical cases including uniformly distributed loading over a plate surface and a concentrated force at the plate center are considered.
However, the response of the models to relatively small perturbations can be reasonably approximated by solving the system of equations obtained by linearization of the model around the initial steady-state.
In this article, attention is focused on the development of an adaptive LUT-based DPD linearization technique which solve the linearization problem in an optimal way.
ILU-preconditioned matrix-free Krylov-based iterative methods are employed to solve the linear systems arising from the successive linearizations.
If the linearization operator is non-self-adjoint, the preconditioned biconjugate-gradient method is proposed to solve the linear equation.
To reduce computational cost associated with solving the optimization problem, Gaussian approximation and linearization are employed for a rapid prediction of the mean square error (MSE) reduction brought by each delayed measurement.
In order to overcome the difficulty of solving the Hamilton Jacobi inequality (HJI), the global linearization technique is employed to simplify the measurement procedure by a simple linear matrix inequality (LMI) method.
Then the modified Magnus integrator method based on local linearization is appropriately designed for solving the above new form.
An algorithm for solving the proposed design constraints by combining a nested bilinear-terms-solver approach and a nonlinear-optimization-based cone complementary linearization method is provided.
The purpose of this article is to propose a numerical method for solving the generalized Burger Fisher equation using CAS wavelets in conjunction with quasi-linearization technique.
Sophus Lie [5] used the idea of point transformations to solve the problem on linearization of second-order ordinary differential equations.
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