Newton-like methods for solving vector optimization problems

In the context of Euclidean spaces, we present an extension of the Newton-like method for solving vector optimization problems, with respect to the partial orders induced by a pointed, closed and convex cone with a nonempty interior. We study both exact and inexact versions of the Newton-like method. Under reasonable hypotheses, we prove stationarity of accumulation points of the sequences produced by Newton-like methods. Moreover, assuming strict cone-convexity of the objective map to the vector optimization problem, we establish convergence of the sequences to an efficient point whenever the initial point is in a compact level set.     

Newton-like methods for two-point boundary value problems

A class of Newton-like methods for discrete two-point boundary value problems is constructed from the sum equation formulation of the problem. Each step of the Newton-like method can be described as first solving a system of linear algebraic equations. The solution vector of this system gives boundary values to a number of discrete boundary value problems which can be solved explicitly.     

Convergence of Newton-like-iterative methods

Summary Newton-like methods in which the intermediate systems of linear equations are solved by iterative techniques are examined. By applying the theory of inexact Newton methods radius of convergence and rate of convergence results are easily obtained. The analysis is carried out in affine invariant terms. The results are applicable to cases where the underlying Newton-like method is, for example, a difference Newton-like or update-Newton method.     

Multilevel least-change Newton-like methods for equality constrained optimization problems

This paper deals with the application of multilevel least-change Newton-like methods for solving twice continuously differentiable equality constrained optimization problems. We define multilevel partial-inverse least-change updates, multilevel least-change Newton-like methods without derivatives and multilevel projections of fragments of the matrix for Newton-like methods without derivatives. Local andq-superlinear convergence of these methods is proved. The theorems here also imply local andq-superlinear convergence of many standard Newton-like methods for nonconstrained and equality constraine optimization problems.     

Convergence of Newton-like methods for solving systems of nonlinear equations

Summary An analysis is given of the convergence of Newton-like methods for solving systems of nonlinear equations. Special attention is paid to the computational aspects of this problem.     

Monotone convergence of Newton-like methods for M-matrix algebraic Riccati equations

For the algebraic Riccati equation whose four coefficient matrices form a nonsingular M-matrix or an irreducible singular M-matrix K, the minimal nonnegative solution can be found by Newton’s method and the doubling algorithm. When the two diagonal blocks of the matrix K have both large and small diagonal entries, the doubling algorithm often requires many more iterations than Newton’s method. In those cases, Newton’s method may be more efficient than the doubling algorithm. This has motivated us to study Newton-like methods that have higher-order convergence and are not much more expensive each iteration. We find that the Chebyshev method of order three and a two-step modified Chebyshev method of order four can be more efficient than Newton’s method. For the Riccati equation, these two Newton-like methods are actually special cases of the Newton–Shamanskii method. We show that, starting with zero initial guess or some other suitable initial guess, the sequence generated by the Newton–Shamanskii method converges monotonically to the minimal nonnegative solution.We also explain that the Newton-like methods can be used to great advantage when solving some Riccati equations involving a parameter.     

Variational Methods for Non-Linear Least-Squares

We consider Newton-like line search descent methods for solving non-linear least-squares problems. The basis of our approach is to choose a method, or parameters within a method, by minimizing a variational measure which estimates the error in an inverse Hessian approximation. In one approach we consider sizing methods and choose sizing parameters in an optimal way. In another approach we consider various possibilities for hybrid Gauss-Newton/BFGS methods. We conclude that a simple Gauss-Newton/BFGS hybrid is both efficient and robust and we illustrate this by a range of comparative tests with other methods. These experiments include not only many well known test problems but also some new classes of large residual problem.     

Majorizing sequences for iterative procedures in Banach spaces

Newton-like methods are often used for solving nonlinear equations. In the present paper, we introduce very general majorizing sequences for Newton-like methods. Then, we provide semi-local convergence results for these methods. The new convergence results can be weaker than in earlier studies. These new results are illustrated by several numerical examples and special cases of Newton-like methods, for which the older convergence conditions do not hold but for which our weaker convergence conditions are satisfied.     

基于凸优函数的Newton类方法的收敛定理

本文研究了求解Banach空间上非线性算子方程f(x)=0的Newton类方法的收敛性.利用优函数原理,在A(x0)1f满足关于某一凸优函数的广义Lipschitz条件下,得到了Newton类方法的一个半局部收敛定理.同时,当f和A(x)及初始点x0给定时,针对广义Lipschitz条件构造了相应的优函数,推广了Newton类方法的相关结果.     

Construction of Newton-like iteration methods for solving nonlinear equations

In this paper, we present a simple, and yet powerful and easily applicable scheme in constructing the Newton-like iteration formulae for the computation of the solutions of nonlinear equations. The new scheme is based on the homotopy analysis method applied to equations in general form equivalent to the nonlinear equations. It provides a tool to develop new Newton-like iteration methods or to improve the existing iteration methods which contains the well-known Newton iteration formula in logic; those all improve the Newton method. The orders of convergence and corresponding error equations of the obtained iteration formulae are derived analytically or with the help of Maple. Some numerical tests are given to support the theory developed in this paper.     

Quasi-Newton projection methods and the discrepancy principle in image restoration

In this work, the problem of the restoration of images corrupted by space invariant blur and noise is considered. This problem is ill-posed and regularization is required. The image restoration problem is formulated as a nonnegatively constrained minimization problem whose objective function depends on the statistical properties of the noise corrupting the observed image. The cases of Gaussian and Poisson noise are both considered. A Newton-like projection method with early stopping of the iterates is proposed as an iterative regularization method in order to determine a nonnegative approximation to the original image. A suitable approximation of the Hessian of the objective function is proposed for a fast solution of the Newton system. The results of the numerical experiments show the effectiveness of the method in computing a good solution in few iterations, when compared with some methods recently proposed as best performing.     

A convergence theorem for Newton-like methods in Banach spaces

Summary A convergence theorem for Newton-like methods in Banach spaces is given, which improves results of Rheinboldt [27], Dennis [4], Miel [15, 16] and Moret [18] and includes as a special case an updated (affine-invariant [6]) version of the Kantorovich theorem for the Newton method given in previous papers [35, 36]. Error bounds obtained in [34] are also improved. This paper unifies the study of finding sharp error bounds for Newton-like methods under Kantorovich type assumptions.Sponsored by the United States Army under Contract No. DAAG29-80-C-0041 and by the Ministry of Education, Japan     

Newton-like methods for efficient solutions in vector optimization

In this work we study the Newton-like methods for finding efficient solutions of the vector optimization problem for a map from a finite dimensional Hilbert space X to a Banach space Y, with respect to the partial order induced by a closed, convex and pointed cone C with a nonempty interior. We present both exact and inexact versions, in which the subproblems are solved approximately, within a tolerance. Furthermore, we prove that under reasonable hypotheses, the sequence generated by our method converges to an efficient solution of this problem.     

使用非单调技术的不精确预条件牛顿类方法解非线性方程组

本文提供了预条件不精确牛顿型方法结合非单调技术解光滑的非线性方程组．在合理的条件下证明了算法的整体收敛性．进一步，基于预条件收敛的性质，获得了算法的局部收敛速率，并指出如何选择势序列保证预条件不精确牛顿型的算法局部超线性收敛速率．     

On the convergence and application of Newton-like methods for analytic operators

We provide local and semilocal theorems for the convergence of Newton-like methods to a locally unique solution of an equation in a Banach space. The analytic property of the operator involved replaces the usual domain condition for Newton-like methods. In the case of the local results we show that the radius of convergence can be enlarged. A numerical example is given to justify our claim. This observation is important and finds applications in steplength selection in predictor-corrector continuation procedures.     

On a class of Newton-like methods for solving nonlinear equations

We provide a semilocal convergence analysis for a certain class of Newton-like methods considered also in [I.K. Argyros, A unifying local-semilocal convergence analysis and applications for two-point Newton-like methods in Banach space, J. Math. Anal. Appl. 298 (2004) 374–397; I.K. Argyros, Computational theory of iterative methods, in: C.K. Chui, L. Wuytack (Eds.), Series: Studies in Computational Mathematics, vol. 15, Elsevier Publ. Co, New York, USA, 2007; J.E. Dennis, Toward a unified convergence theory for Newton-like methods, in: L.B. Rall (Ed.), Nonlinear Functional Analysis and Applications, Academic Press, New York, 1971], in order to approximate a locally unique solution of an equation in a Banach space.     

An interval arithmetic domain decomposition method for a class of elliptic PDEs on nonrectangular domains

We introduce an interval arithmetic domain decomposition method for linear systems with interval coefficients resulting from the application of difference methods for a class of elliptic boundary value problems on domains with irregular geometry. The efficient treatment of such systems is crucial for the efficiency of globally convergent Newton-like interval methods for the corresponding nonlinear problems.     

Issues in the implementation of the DSD algorithm for the traffic assignment problem

In this paper we consider the practical implementation of the disaggregated simplicial decomposition (DSD) algorithm for the traffic assignment problem. It is a column generation method that at each step has to solve a huge number of quadratic knapsack problems (QKP). We propose a Newton-like method to solve the QKP when the quadratic functional is convex but not necessarily strictly. Our O(n) algorithm does not improve the complexity of the current methods but extends them to a more general case and is better suited for reoptimization and so a good option for the DSD algorithm. It also allows the solution of many QKP’s simultaneously in a vectorial or parallel way.     

A Newton-like method for nonsmooth variational inequalities

We present new results for the local convergence of the Newton-like method to a unique solution of nondifferentiable variational inclusions in a Banach space setting using the Lipschitz-like property of set-valued mappings and the concept of slant differentiability hypothesis on the operator involved, as was introduced by X. Chen, Z. Nashed and L. Qi. The linear convergence of the Newton-like method is also established. Our results extend the applicability of the Newton-like method (Argyros and Hilout, 2009 [5] and Chen, Nashed and Qi, 2000 [7]) to variational inclusions.     

A Ulm-like method for inverse eigenvalue problems

We propose a Ulm-like method for solving inverse eigenvalue problems, which avoids solving approximate Jacobian equations comparing with other known methods. A convergence analysis of this method is provided and the R-quadratic convergence property is proved under the assumption of the distinction of given eigenvalues. Numerical experiments as well as the comparison with the inexact Newton-like method are given in the last section.