A new semi-local convergence theorem for the inexact Newton methods

We present a new semi-local convergence theorem for the inexact Newton methods in the assumption that the derivative satisfies some kind of weak Lipschitz conditions. As special cases of our main result we re-obtain some well-known convergence theorems for Newton methods.     

A local convergence property of primal-dual methods for nonlinear programming

We prove a new local convergence property of some primal-dual methods for solving nonlinear optimization problems. We consider a standard interior point approach, for which the complementarity conditions of the original primal-dual system are perturbed by a parameter driven to zero during the iterations. The sequence of iterates is generated by a linearization of the perturbed system and by applying the fraction to the boundary rule to maintain strict feasibility of the iterates with respect to the nonnegativity constraints. The analysis of the rate of convergence is carried out by considering an arbitrary sequence of perturbation parameters converging to zero. We first show that, once an iterate belongs to a neighbourhood of convergence of the Newton method applied to the original system, then the whole sequence of iterates converges to the solution. In addition, if the perturbation parameters converge to zero with a rate of convergence at most superlinear, then the sequence of iterates becomes asymptotically tangent to the central trajectory in a natural way. We give an example showing that this property can be false when the perturbation parameter goes to zero quadratically.      

A two-step Steffensen's method under modified convergence conditions

A simplification of a third order iterative method is proposed. The main advantage of this method is that it does not need to evaluate neither any Fréchet derivative nor any bilinear operator. A semilocal convergence theorem in Banach spaces, under modified Kantorovich conditions, is analyzed. A local convergence analysis is also performed. Finally, some numerical results are presented.     

CURVILINEAR PATHS AND TRUST REGION METHODS WITH NONMONOTONIC BACK TRACKING TECHNIQUE FOR UNCONSTRAINED OPTIMIZATION

1. Illtroductioncrust region method is a well-accepted technique in nonlinear optindzation to assure globalconvergence. One of the adVantages of the model is that it does not require the objectivefunction to be convex. Many differellt versions have been suggested in using trust regiontechnique. For each iteration, suppose a current iterate point, a local quadratic model of thefunction and a trust region with center at the point and a certain radius are given. A point thatminimizes the model f…     

Relationships between different types of initial conditions for simultaneous root finding methods

The construction of initial conditions of an iterative method is one of the most important problems in solving nonlinear equations. In this paper, we obtain relationships between different types of initial conditions that guarantee the convergence of iterative methods for simultaneously finding all zeros of a polynomial. In particular, we show that any local convergence theorem for a simultaneous method can be converted into a convergence theorem with computationally verifiable initial conditions which is of practical importance. Thus, we propose a new approach for obtaining semilocal convergence results for simultaneous methods via local convergence results.     

矩阵多分裂迭代法的收敛条件

本文给出了求解非奇异线性方程组的矩阵多分裂并行迭代法的一些新的收敛结果.当系数矩阵单调和多分裂序列为弱正则分裂时,得到了几个与已有的收敛准则等价的条件,并且证明了异步迭代法在较弱条件下的收敛性.对于同步迭代,给出了与异步迭代不同且较为宽松的收敛条件.     

Global convergence and stabilization of unconstrained minimization methods without derivatives

In this paper, acceptability criteria for the stepsize and global convergence conditions are established for unconstrained minimization methods employing only function values. On the basis of these results, the convergence of an implementable line search algorithm is proved and some global stabilization schemes are described.The authors would like to thank the anonymous referees for their useful suggestions.     

Necessary and sufficient conditions for the convergence of iterative methods for the linear complementarity problem

Necessary and sufficient conditions are established for the convergence of various iterative methods for solving the linear complementarity problem. The fundamental tool used is the classical notion of matrix splitting in numerical analysis. The results derived are similar to some well-known theorems on the convergence of iterative methods for square systems of linear equations. An application of the results to a strictly convex quadratic program is also given.This research was based on work supported by the National Science Foundation under Grant No. ECS-81-14571.The author gratefully acknowledges several comments by K. Truemper on the topics of this paper.     

A generalized subgradient method with relaxation step

We study conditions for convergence of a generalized subgradient algorithm in which a relaxation step is taken in a direction, which is a convex combination of possibly all previously generated subgradients. A simple condition for convergence is given and conditions that guarantee a linear convergence rate are also presented. We show that choosing the steplength parameter and convex combination of subgradients in a certain sense optimally is equivalent to solving a minimum norm quadratic programming problem. It is also shown that if the direction is restricted to be a convex combination of the current subgradient and the previous direction, then an optimal choice of stepsize and direction is equivalent to the Camerini—Fratta—Maffioli modification of the subgradient method.Research supported by the Swedish Research Council for Engineering Sciences (TFR).     

New inexact implicit method for general mixed quasi variational inequalities

In this paper, we suggest and analyze a new self-adaptive inexact implicit method with a variable parameter for general mixed quasi variational inequalities, where the skew-symmetry of the nonlinear bifunction plays a crucial part in the convergence analysis of this method. We use a self-adaptive technique to adjust parameter ρ at each iteration. The global convergence of the proposed method is proved under some mild conditions. Preliminary numerical results indicate that the self-adaptive adjustment rule is necessary in practice. Muhammad Aslam Noor is supported by the Higher Education Commission, Pakistan, through research grant No: 1-28/HEC/HRD/2005/90.     

LC1约束规划问题的超线性收敛ODE型信赖域算法

本文对带线性等式约束的LC^1优化问题提出了一个新的ODE型信赖域算法，它在每一次迭代时，不必求解带信赖域界的子问题，仅解一线性方程组而求得试验步。从而可以降低计算的复杂性，提高计算效率，在一定的条件下，文中还证明了该算法是超线性收敛的。     

Bilateral approximations for some Aitken-Steffensen-Hermite type methods of order three

We study the local convergence of some Aitken-Steffensen-Hermite type methods of order three. We obtain that under some reasonable conditions on the monotony and convexity of the nonlinear function, the iterations offer bilateral approximations for the solution, which can be efficiently used as a posteriori estimations.     

A robust algebraic multilevel preconditioner for non‐symmetric M‐matrices

Stable finite difference approximations of convection‐diffusion equations lead to large sparse linear systems of equations whose coefficient matrix is an M‐matrix, which is highly non‐symmetric when the convection dominates. For an efficient iterative solution of such systems, it is proposed to consider in the non‐symmetric case an algebraic multilevel preconditioning method formerly proposed for pure diffusion problems, and for which theoretical results prove grid independent convergence in this context. These results are supplemented here by a Fourier analysis that applies to constant coefficient problems with periodic boundary conditions whenever using an ‘idealized’ version of the two‐level preconditioner. Within this setting, it is proved that any eigenvalue λ of the preconditioned system satisfies for some real constant c such that . This result holds independently of the grid size and uniformly with respect to the ratio between convection and diffusion. Extensive numerical experiments are conducted to assess the convergence of practical two‐ and multi‐level schemes. These experiments, which include problems with highly variable and rotating convective flow, indicate that the convergence is grid independent. It deteriorates moderately as the convection becomes increasingly dominating, but the convergence factor remains uniformly bounded. This conclusion is supported for both uniform and some non‐uniform (stretched) grids. Copyright © 2000 John Wiley & Sons, Ltd.     

Third-order iterative methods under Kantorovich conditions

We study a class of third-order iterative methods for nonlinear equations on Banach spaces. A characterization of the convergence under Kantorovich type conditions and optimal estimates of the error are found. Though, in general, these methods are not very extended due to their computational costs, we will show some examples in which they are competitive and even cheaper than other simpler methods. We center our analysis in both, analytic and computational, aspects.     

一个带固定步长的ODE型算法

提出了一种新的求解无约束优化问题的ODE型方法,其特点是:它在每次迭代时仅求解一个线性方程组系统来获得试探步;若该试探步不被接受,算法就沿着该试探步的方向求得下一个迭代点,其中步长通过固定公式计算得到.这样既避免了传统的ODE型算法中为获得可接受的试探步而重复求解线性方程组系统,又不必执行线搜索,从而减少了计算量.在适当的条件下,还证明了新算法的整体收敛性和局部超线性收敛性.数值试验结果表明:提出的算法是有效的.     

Application of the method of multipliers to state space constrained optimal control problems with discrete time

The paper deals with convergence conditions of multiplier algorithms for solving optimal control problems with discrete time suggested by J. Bjbvonek in some earlier papers. In this approach the original state space constrained problem is converted into a control-constrained problem by introducing an additional control variable and an equality constraint which is taken into consideration by a multiplier method. Convergence conditions for the multiplier Iteration of global and local nature are given for exact and inexact solution of the subproblems.     

Modified Goldstein–Levitin–Polyak Projection Method for Asymmetric Strongly Monotone Variational Inequalities

In this paper, we present a modified Goldstein–Levitin–Polyak projection method for asymmetric strongly monotone variational inequality problems. A practical and robust stepsize choice strategy, termed self-adaptive procedure, is developed. The global convergence of the resulting algorithm is established under the same conditions used in the original projection method. Numerical results and comparison with some existing projection-type methods are given to illustrate the efficiency of the proposed method.     

Propagation of round-off errors and the role of stability in numerical methods for linear and nonlinear PDEs

It is shown that the customary assumption on the propagation of round-off errors in numerical methods for PDEs is unrealistic, as it yields a convergence result which is better than the best possible similar convergence result for ODEs. A solution is suggested by which round-off errors can be modelled by smooth functions, with consequent weakening of overall stability conditions and improvement of convergence conditions.     

A quasi-Newton trust-region method

The classical trust-region method for unconstrained minimization can be augmented with a line search that finds a point that satisfies the Wolfe conditions. One can use this new method to define an algorithm that simultaneously satisfies the quasi-Newton condition at each iteration and maintains a positive-definite approximation to the Hessian of the objective function. This new algorithm has strong global convergence properties and is robust and efficient in practice.     

The convergence analysis of inexact Gauss–Newton methods for nonlinear problems

In this paper, inexact Gauss–Newton methods for nonlinear least squares problems are studied. Under the hypothesis that derivative satisfies some kinds of weak Lipschitz conditions, the local convergence properties of inexact Gauss–Newton and inexact Gauss–Newton like methods for nonlinear problems are established with the modified relative residual control. The obtained results can provide an estimate of convergence ball for inexact Gauss–Newton methods.