一类求解非线性奇异方程组的牛顿改进算法

受一个求解非线性奇异方程组迭代格式的启示,将两种牛顿改进算法推广成一般形式,并将其发展为一类求解具有奇异雅可比矩阵的非线性方程组的牛顿改进算法.首先,描述这类新算法的迭代格式,并导出其收敛阶,该新格式每步迭代仅需计算一次函数值和一次导函数值;然后,对测试函数进行检验,并与牛顿算法及其他奇异牛顿算法进行比较,从而验证该算法的快速收敛性;最后,通过两个实际问题验证所提出算法的有效性.     

求解非线性方程组的改进不精确雅可比牛顿法

在分析不精确雅可比牛顿法的基础上,进一步研究了不精确雅可比矩阵在精确解附近奇异的求解方法。利用雅可比矩阵与函数自身,在不增加新的计算量前提下,得到改进的求解非线性方程组的不精确雅可比牛顿算法。数值结果表明,改进后算法与原不精确雅可比牛顿法具有相同的计算效率,而且在使用上更为方便,有效。     

两种解非线性方程组的四阶迭代方法

本文给出了两种新的解非线性方程组的迭代方法,证明了它们具有四阶收敛性,通过数值实例对几种不同的迭代方法和本文提出的两种新方法进行了分析比较,说明了本文方法的有效性.     

一类Lyapunov 型矩阵方程组的中心对称解及其最佳逼近

建立了求矩阵方程组AiXBi＋GiXDi=Fi（i=1,2）的中心对称解的迭代算法.使用该方法不仅可以判断矩阵方程组是否有中心对称解,而且在有中心对称解时,还能够在有限步迭代计算之后得到矩阵方程组的极小范数中心对称解.同时,也能够在矩阵方程组的中心对称解集合中求得给定矩阵的最佳逼近.     

求解非线性方程组的迭代神经网络算法

求解非线性方程组是工程研究中的基本问题,普通的求解算法均具有一定的缺点,通用性不强。神经网络能以任意精度逼近非线性函数,利用它逼近非线性方程组的函数的反函数,提出了通用性较强的数值求解方法。首先,给出了不需迭代的简单神经网络算法;然后,针对给定求解区域偏大和不准确的问题,提出了缩小与改变求解区域的迭代神经网络算法。这两种算法均进行了实例求解,结果表明,两种算法格式简单,求解时间短,精度高,具有较高的应用价值,在理论研究和工程实践中具有较大应用前景。最后分析了算法的优点和改进方向。     

求解非线性方程组的蚁群算法

研究非线性方程组的求解问题,提高有效性。针对非线性方程数与变量数一致的非线性方程组问题,当方程组是一些强非线性方程组时,传统方法易导致失败,有效率低。为了提高求解强非线性方程组的求解效率,提出一种蚁群算法的求解方法。首先将方程组问题转化为函数优化问题,然后用全局搜索速度快的蚁群算法对函数进行求解,找到最优解,最后通过具体实例进行仿真研究,结果表明蚁群算法的有效性。     

单纯形法的改进萤火虫算法及其在非线性方程组求解中的应用

萤火虫算法(FA)是一种基于群体搜索的启发式随机优化算法,其模拟自然界中萤火虫利用发光的生物学特性而表现出来的社会性行为。针对萤火虫算法存在着收敛速度慢、易陷入局部最优、求解精度低等不足,利用单纯形法局部搜索速度快和萤火虫算法全局寻优的特点,提出一种基于单纯形法的改进型萤火虫算法(SMFA)。通过对标准测试函数以及非线性方程组的实验仿真,并与其他算法进行的对比分析表明,改进后的算法在函数优化方面有较强的优势,在一定程度上有效地避免了陷入局部最优,提高了搜索的精度。     

求解非线性方程组的社会认知算法

将非线性方程组的求解问题转化为函数优化问题,应用一种新的智能优化算法——社会认知算法求解此优化问题,实验结果表明了社会认知算法在求解非线性方程组时的可行性和有效性。     

一类具有记忆项的耦合非线性抽象方程组的整体解

运用Galerkin方法讨论了一类具有记忆项的耦合非线性抽象方程组的初值问题,根据方程组的特点,巧妙地对两个方程进行相加,并结合微积分的性质得到了所要的结果,然后研究收敛性,最后证明了方程组整体弱解的存在性.     

基于区间-遗传算法求解非线性方程组

将非线性方程组的求解转化为函数优化问题,结合遗传算法的群体搜索、全局收敛的优点,及区间算法特有的解的存在性检验准则,提出了一种区间—遗传算法。在迭代计算过程中,区间算法为遗传算法搜索提供可靠区域,同时遗传算法为区间算法提供安全的初始区域。数值实验表明,该算法能够在较大范围的初始区间内快速,可靠地迭代得到高精度的区间解,是求解非线性方程组的一种有效的算法。     

Incomplete Jacobian Newton method for nonlinear equations

This paper presents a new modified Newton method for nonlinear equations. This method uses a part of elements of the Jacobian matrix to obtain the next iteration point and is refereed to as the incomplete Jacobian Newton (IJN) method. The IJN method may be fit for solving large scale nonlinear equations with dense Jacobian. The conditions of linear, superlinear and quadratic convergence of the IJN method are given and the local convergence results are analyzed and proved. Some special IJN algorithms are designed and numerical experiments are given. The results show that the IJN method is promising.     

一族避免二阶求导的四阶迭代法

从一族解非线性方程的带参数的三阶迭代法出发,推出避免计算二阶导数的迭代族.它只需计算一阶导数值,但收敛速度却更高,至少具有四阶的收敛速度,它与别的同类型方法相比具有形式简单、计算量少等特点.最后给出数值实验,从数值实验可以看出新方法是非常有效的.     

Pseudo-transient continuation-based variable relaxation solver in nonlinear magnetohydrodynamic simulations

Efficient and robust Variable Relaxation Solver, based on pseudo-transient continuation, is developed to solve nonlinear anisotropic thermal conduction arising from fusion plasma simulations. By adding first- and/or second-order artificial time derivatives to the system, this type of method advances the resulting time-dependent nonlinear PDEs to steady state, which is the solution to be sought. In this process, only the stiffness matrix itself is involved so that the numerical complexity and errors can be greatly reduced. In fact, this work is an extension of integrating efficient linear elliptic solvers for fusion simulation on Cray X1E. Two schemes are derived in this work, first- and second-order variable relaxations. Four factors are observed to be critical for efficiency and preservation of solution's symmetric structure arising from periodic boundary condition: refining meshes in different coordinate directions, initializing nonlinear process, varying time steps in both temporal and spatial directions, and accurately generating nonlinear stiffness matrix. First finer mesh scale should be taken in strong transport direction; next the system is carefully initialized by the solution with linear conductivity; third, time step and relaxation factor are vertex-based varied and optimized at each time step; finally, the nonlinear stiffness matrix is updated by just scaling corresponding linear one with the vector generated from nonlinear thermal conductivity.     

ASYNCHRONOUS MONOTONE NEWTON ITERATIVE METHOD ON DISTRIBUTED COMPUTERS

In this paper, according to the asynchronous iteration model presented by G. M. Baudet [4] and D.P. Bertsekas [5], we propose an asynchronous monotone Newton iterative method for solving the nonlinear system of equations F(x) = 0 on a distributed computer, and prove its convergence.     

求解非线性方程重根的区间牛顿法

讨论了求解非线性方程重根问题,针对此时Moore区间牛顿法不再适用,以及Hansen改进的区间牛顿法收敛速度慢的情况,通过引入原方程的一种相关方程,建立了求解非线性方程重根的区间牛顿法;证明了其局部平方收敛的性质,给出了数值算例。验证了新算法比Hansen改进的区间牛顿法具有更快的收敛速度,且算法是有效和可靠的。     

Convergence of the modified SOR–Newton method for non-smooth equations

Motivated by Chen [On the convergence of SOR methods for nonsmooth equations. Numer. Linear Algebra Appl. 9 (2002), pp. 81–92], in this paper, we further investigate a modified SOR–Newton (MSOR–Newton) method for solving a system of nonlinear equations F(x)=0, where F is strongly monotone and locally Lipschitz continuous but not necessarily differentiable. The convergence interval of the parameter in the MSOR–Newton method is given. Compared with that of the SOR–Newton method, this interval can be enlarged. Furthermore, when the B-differential of F(x) is difficult to compute, a simple replacement can be used, which can reduce the computational load. Numerical examples show that at the same cost of computational complexity, this MSOR–Newton method can converge faster than the corresponding SOR–Newton method by choosing a suitable parameter.     

An implicit iterative algorithm with a tuning parameter for Itô Lyapunov matrix equations

In this paper, an implicit iterative algorithm is proposed for solving a class of Lyapunov matrix equations arising in Itô stochastic linear systems. A tuning parameter is introduced in this algorithm, and thus the convergence rate of the algorithm can be changed. Some conditions are presented such that the developed algorithm is convergent. In addition, an explicit expression is also derived for the optimal tuning parameter, which guarantees that the obtained algorithm achieves its fastest convergence rate. Finally, numerical examples are employed to illustrate the effectiveness of the given algorithm.     

快速收敛参考独立分量分析方法

为提高参考独立分量分析收敛速度,提出了改进的快速收敛参考独立分量分析方法.该方法首先采用预白化方法对观测到的信号进行处理,降低后续处理算法的复杂度,然后采用收敛速度更快、结构更简单的牛顿迭代方法对其进行优化,提高其收敛速度.理论分析表明,改进后的算法相对现有算法每次迭代的计算量基本相同;实验仿真结果表明,相对现有方法,...