Inexact Newton method via Lanczos decomposed technique for solving box-constrained nonlinear systems

This paper proposes an inexact Newton method via the Lanczos decomposed technique for solving the box-constrained nonlinear systems. An iterative direction is obtained by solving an affine scaling quadratic model with the Lanczos decomposed technique. By using the interior backtracking line search technique, an acceptable trial step length is found along this direction. The global convergence and the fast local convergence rate of the proposed algorithm are established under some reasonable conditions. Furthermore, the results of the numerical experiments show the effectiveness of the pro- posed algorithm.

ON SEMILOCAL CONVERGENCE OF INEXACT NEWTON METHODS

Inexact Newton methods are constructed by combining Newton＇s method with another iterative method that is used to solve the Newton equations inexactly. In this paper, we establish two semilocal convergence theorems for the inexact Newton methods. When these two theorems are specified to Newton＇s method, we obtain a different Newton-Kantorovich theorem about Newton＇s method. When the iterative method for solving the Newton equations is specified to be the splitting method, we get two estimates about the iteration steps for the special inexact Newton methods.

A Filter Line Search Algorithm Based on an Inexact Newton Method for Nonconvex Equality Constrained Optimization

We propose an inexact Newton method with a filter line search algorithm for nonconvex equality constrained optimization. Inexact Newton＇s methods are needed for large-scale applications which the iteration matrix cannot be explicitly formed or factored. We incorporate inexact Newton strategies in filter line search, yielding algorithm that can ensure global convergence. An analysis of the global behavior of the algorithm and numerical results on a collection of test problems are presented.

Fully-Coupled Multi-Physical Simulation with Physics-Based Nonlinearity-Elimination Preconditioned Inexact Newton Method for Enhanced Oil Recovery

In this paper,we introduce a physics-based nonlinear preconditioned Inexact Newton Method(INB)for the multiphysical simulation of fractured reservoirs.Instead of solving the partial differential equations(PDE)exactly,Inexact Newton method finds a direction for the iteration and solves the equations inexactly with fewer iterations.However,when the equations are not smooth enough,especially when lo-cal discontinuities exits,and when proper preconditioning operations are not adopted,the Inexact Newton method may be slow or even stagnant.As pointed out by Keyes et al.[1],multi-physical numerical simulation faces several challenges,one of which is the local-scale nonlinearity and discontinuity.In this work,we have proposed and studied a nonlinear preconditioner to improve the performance of Inexact Newton Method.The nonlinear preconditioner is essentially a physics-based strategy to adaptively identify and eliminate the highly nonlinear zones.The proposed algorithm has been implemented into our fully coupled,fully implicit THM reservoir simulator(Wang et al.[2,3])to study the effects of cold water injection on fractured petroleum reservoirs.The results of this work show that after the implementation of this nonlinear preconditioner,the iterative solver has become significantly more robust and efficient.

A smoothing inexact Newton method for P0 nonlinear complementarity problem

We first propose a new class of smoothing functions for the non- linear complementarity function which contains the well-known Chen-Harker- Kanzow-Smale smoothing function and Huang-Han-Chen smoothing function as special cases, and then present a smoothing inexact Newton algorithm for the P0 nonlinear complementarity problem. The global convergence and local superlinear convergence are established. Preliminary numerical results indicate the feasibility and efficiency of the algorithm.

CONVERGENCE OF INEXACT CONIC NEWTON METHODS

A conic Newton method is attractive because it converges to a local minimizzer rapidly from any sufficiently good initial guess. However, it may be expensive to solve the conic Newton equation at each iterate. In this paper we consider an inexact conic Newton method, which solves the couic Newton equation oldy approximately and in sonm unspecified manner. Furthermore, we show that such method is locally convergent and characterizes the order of convergence in terms of the rate of convergence of the relative residuals.

GLOBAL COVERGENCE OF THE NON-QUASI-NEWTON METHOD FOR UNCONSTRAINED OPTIMIZATION PROBLEMS

In this paper, the non-quasi-Newton＇s family with inexact line search applied to unconstrained optimization problems is studied. A new update formula for non-quasi-Newton＇s family is proposed. It is proved that the constituted algorithm with either Wolfe-type or Armijotype line search converges globally and Q-superlinearly if the function to be minimized has Lipschitz continuous gradient.

求解大规模混合互补问题的Newton-GMRES方法

针对大规模混合互补问题,借助Chen-Harker-Kanzow-Smale(CHKS)光滑逼近函数,将其转化为光滑方程组。与非精确牛顿法相结合,得到了一种求解混合互补问题的Newton-GMRES算法,证明了该算法的全局收敛性。数值实验结果表明,该算法可行有效。

求解大规模加权线性互补问题的非精确Newton-GMRES算法

针对加权线性互补问题,给出一个新的光滑函数,将其转化为光滑方程组,并与非精确牛顿法结合,得到了一种求解加权线性互补问题的非精确Newton-GMRES算法.证明了该算法的全局收敛性,数值实验表明该算法稳定有效.

不精确Newton法与Broyden法的仿射不变收敛性

给出了不精确Ｎｅｗｔｏｎ法的半局部收敛性定理，通过改善条件γ＿ｋ／Ｆ（ｘ￣ｋ）≤η＿ｋ（ｋ＝０，１…）使其具仿射不变性，建立起了不精确Ｎｅｗｔｏｎ法的具仿射不变性的半局部收敛定理，在一定条件下，讨论了Ｂｒｏｙｄｅｎ方法的具仿射不变性的存在──收敛定理，从而，扩大了这两种方法收敛定理的收敛域。

非线性方程组的非交替Newton-PHSS迭代法

大型稀疏非Hermite正定Jacobi矩阵对应的非线性方程组的迭代求解历来受到重视.结合不精确Newton法和非交替PHSS迭代法,提出了迭代求解非线性方程组的NewtonNPHSS方法,给出了迭代法的局部收敛定理,并演算了数值例子,阐明了Newton-NPHSS是有效的迭代法.

用非精确Newton-Moser型方法求解奇异问题

讨论了用非精确Newton-Moser法求解奇异问题,证明了该方法的收敛性.并给出了误差估计.

Newton-LHSS后退方法及其全局收敛性的研究

基于倾向一侧的HSS(LHSS)方法,提出了一类求解非线性方程组的Newton-LHSS后退(NLHSSB)方法,给出了Newton-LHSS后退方法的全局收敛定理.数值实验证明了该方法的正确性和有效性.

求解半光滑方程组的非精确Newton法

1977年Mifflin提出了半光滑函数这一概念。Qi与Sun将它推广到映射的情形。映射F :Rn→Rm 在x点被称为是半光滑的 ,若F在x附近是Lipschitz连续的 ,且对任意的h∈Rn,极限limV∈F(x +th′)h′→h ,t↓ 0{Vh′]存在。半光滑函数 (映射 )类覆盖许多的重要函数 ,如光滑函数、凸函数、分片光滑函数 ,半光滑函数的复合函数等。给出半光滑方程组的非精确Newton算法 ,证明了产生的序列是局部收敛的 ,且在一定条件下收敛速度是超线性的 。

可对称化不定非线性问题的不精确Newton法

本文讨论了处理可对称化不定问题的不精确Newton方法,并针对问题的特殊结构提出了不精确Newton-PSMINRES算法。理论分析与数值试验表明,Newton-PSMINRES算法优于其它处理可对称化不定问题的不精确Newton-Krylov算法。

关于Newton-like-iterative方法新的收敛性定理(英文)

用迭代法求解Newton-like法中的方程,T.J . Ypma提出Newton-like-iterative方法。在其早期的文章中,不精确牛顿法理论用来研究Newton-like-iterative方法的收敛性。与以往方法不同,今提出用不精确Newton-like法做相关的收敛性分析,所得定理更加简单,同时具有仿射不变性。

一类非线性代数方程组的Newton-Triangle Splitting迭代法

Triangle Splitting迭代方法是求解大型稀疏非Hermitian正定线性代数方程组的一种有效迭代算法.为了有效求解大型稀疏且Jacobi矩阵为非Hermitian正定的非线性代数方程组,本文将Triangle Splitting迭代方法作为不精确Newton方法的内迭代求解器,构造了不精确Newton-Triangle Splitting迭代方法.在适当的约束条件下,给出了该方法的两类局部收敛性定理.通过数值实验结果验证了该方法的可行性和有效性,并说明了该方法在计算时间和迭代次数方面比Newton-BTSS迭代方法更有优势.

非线性方程组自反解的非精确Newton-MCG算法

针对源于科学计算和工程应用领域的非线性代数方程组,本文应用Newton算法求其自反解,并采用修正共轭梯度法(MCG算法)求由Newton算法每一步迭代计算导出的线性代数方程组的近似自反解或其近似自反最小二乘解,建立了求其自反解的非精确Newton-MCG算法.基于MCG算法适用面宽和有限步收敛的特点,建立的非精确Newton-MCG算法仅要求非线性代数方程组有自反解,而不要求它的自反解唯一.数值算例表明,非精确Newton-MCG算法是有效的.

非拟Newton族的导出及其收敛性

对无约束最优化问题提出了一类非拟Ｎｅｗｔｏｎ族算法，它不再是Ｈｕａｎｇ族中的成员．与拟Ｎｅｗｔｏｎ法相比，新给出的校正公式，在不增加计算量的前提下，能吸纳更多的信息，且仍保持正定对称传递性．对一致凸的目标函数，证明了算法的整体收敛性．且结论对众多类型的精确与非精确线搜索均能成立，而这些线搜索在最优化算法中是比较有效且常用的．

非精确Newton法的半局部收敛性

通过引入中心γ0-条件及γ-条件,研究了非精确Newton法的半局部收敛性问题,得到了更优的半局部收敛性分析及更精确的误差估计.