首页> 外文期刊>Computer Modeling in Engineering & Sciences >An Iterative Algorithm for Solving a System of Nonlinear Algebraic Equations, F(x) = 0, Using the System of ODEs with an Optimum α in x = λ [αF + (1 - α)B~TF]; B_(ij)= {partial deriv}F_i/{partial deriv}x_j
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An Iterative Algorithm for Solving a System of Nonlinear Algebraic Equations, F(x) = 0, Using the System of ODEs with an Optimum α in x = λ [αF + (1 - α)B~TF]; B_(ij)= {partial deriv}F_i/{partial deriv}x_j

机译:一种迭代算法,用于求解非线性代数方程组F(x)= 0,使用x =λ时具有最佳α的ODE系统[αF+(1-α)B〜TF]; B_(ij)= {偏导} F_i / {偏导} x_j

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In this paper we solve a system of nonlinear algebraic equations (NAEs) of a vector-form: F(x) = 0. Based-on an invariant manifold defined in the space of (x, t) in terms of the residual-norm of the vector F(x), we derive a system of nonlinear ordinary differential equations (ODEs) with a fictitious time-like variable t as an independent variable: x = λ [αF + (1 - α)B~TF], where λ and α are scalars and B_(ij) = {partial deriv}F_i/{partial deriv}x_j. From this set of nonlinear ODEs, we derive a purely iterative algorithm for finding the solution vector x, without having to invert the Jacobian (tangent stiffness matrix) B. Here, we introduce three new concepts of attracting set, bifurcation and optimal combination, which are controlled by two parameters γ and α. Because we have derived all the related quantities explicitly in terms of F and its differentials, the attracting set, and an optimal α can be derived exactly. When γ changes from zero to a positive value the present algorithms undergo a Hopf bifurcation, such that the convergence speed is much faster than that by using γ=0. Moreover, when the optimal α is used we can further accelerate the convergence speed several times. Some numerical examples are used to validate the performance of the present algorithms, which reveal a very fast convergence rate in finding the solution, and which display great efficiencies and accuracies than achieved before.
机译:在本文中,我们解决了一个矢量形式的非线性代数方程组(NAE):F(x)=0。基于在(x,t)空间中根据剩余范数定义的不变流形对于向量F(x),我们推导了一个虚拟的常微分方程(ODE)系统,其中虚拟时间变量t为自变量:x =λ[αF+(1-α)B〜TF],其中λ和α是标量,并且B_(ij)= {偏导} F_i / {偏导} x_j。从这组非线性ODE中,我们导出了用于求解解矢量x的纯迭代算法,而无需对Jacobian(切线刚度矩阵)B进行求逆。在这里,我们介绍了吸引集,分叉和最优组合的三个新概念,它们由两个参数γ和α控制。因为我们已经根据F及其微分明确导出了所有相关量,所以可以精确地得出吸引集和最佳α。当γ从零变为正值时,本算法经历Hopf分叉,使得收敛速度比通过使用γ= 0的收敛速度快得多。此外,当使用最佳α时,我们可以进一步加快收敛速度​​几倍。一些数值示例用于验证本算法的性能,这些算法显示出找到解决方案的非常快的收敛速度,并且显示出比以前更高的效率和准确性。

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