A model trust-region modification of Newton's method for nonlinear two-point boundary-value problems

The method of quasilinearization for nonlinear two-point boundary-value problems is Newton's method for a nonlinear differential operator equation. A model trust-region approach to globalizing the quasilinearization algorithm is presented. A double-dogleg implementation yields a globally convergent algorithm that is robust in solving difficult problems.This work was supported in part by DOE Contract DE-AS05-82-ER13016 and NSF Grant RII-89-17691 and was part of the author's doctoral thesis at Rice University. It is a pleasure to thank the author's thesis advisors, Professor J. E. Dennis, Jr., and Professor R. A. Tapia.     

Attractive cycles in the iteration of meromorphic functions

Summary The existence of attractive cycles constitutes a serious impediment to the solution of nonlinear equations by iterative methods. This problem is illustrated in the case of the solution of the equationz tanz=c, for complex values ofc, by Newton's method. Relevant results from the theory of the iteration of rational functions are cited and extended to the analysis of this case, in which a meromorphic function is iterated. Extensive numerical results, including many attractive cycles, are summarized.This work was supported in part by the Natural Sciences and Engineering Research Council of Canada under grants A3028 and A7691     

The augmented lagrangian method for equality and inequality constraints in hilbert spaces

In this paper we consider an augmented Lagrangian method for the minimization of a nonlinear functional in the presence of an equality constraint whose image space is in a Hilbert space, an inequality constraint whose image space is finite dimensional, and an affine inequality constraint whose image space is in an infinite dimensional Hilbert space. We obtain local convergence of this method without imposing strict complementarity conditions when the equality, as well as the inequality constraint with finite dimensional image space are augmented. To the author's knowledge this result even generalizes the convergence results which are known when all spaces are finite dimensional.This research was supported by the Air Force Office of Scientific Research under Grant AFOSR-84-0398 and AFOSR-85-0303, by the National Aeronautics and Space Administration under Grant NAG-1-517, and by NSF under Grant UINT-8521208.This research was supported in part by the Fonds zur Förderung der wissenschaftichen Forschung under S3206 and P6005 and by AFOSR-84-0398. Part of this work was performed while the author was visiting the Division of Applied Mathematics, Brown University, Providence, RI, USA.     

A nonlinear programming algorithm for solving semidefinite programs via low-rank factorization

 In this paper, we present a nonlinear programming algorithm for solving semidefinite programs (SDPs) in standard form. The algorithm's distinguishing feature is a change of variables that replaces the symmetric, positive semidefinite variable X of the SDP with a rectangular variable R according to the factorization X=RR ^T . The rank of the factorization, i.e., the number of columns of R, is chosen minimally so as to enhance computational speed while maintaining equivalence with the SDP. Fundamental results concerning the convergence of the algorithm are derived, and encouraging computational results on some large-scale test problems are also presented. Received: March 22, 2001 / Accepted: August 30, 2002 Published online: December 9, 2002 Key Words. semidefinite programming – low-rank factorization – nonlinear programming – augmented Lagrangian – limited memory BFGS This research was supported in part by the National Science Foundation under grants CCR-9902010, INT-9910084, CCR-0203426 and CCR-0203113     

The Lagrangian Globalization Method for Nonsmooth Constrained Equations

The difficulty suffered in optimization-based algorithms for the solution of nonlinear equations lies in that the traditional methods for solving the optimization problem have been mainly concerned with finding a stationary point or a local minimizer of the underlying optimization problem, which is not necessarily a solution of the equations. One method to overcome this difficulty is the Lagrangian globalization (LG for simplicity) method. This paper extends the LG method to nonsmooth equations with bound constraints. The absolute system of equations is introduced. A so-called Projected Generalized-Gradient Direction (PGGD) is constructed and proved to be a descent direction of the reformulated nonsmooth optimization problem. This projected approach keeps the feasibility of the iterates. The convergence of the new algorithm is established by specializing the PGGD. Numerical tests are given. This author's work was done when she was visiting The Hong Kong Polytechnic University. His work is also supported by the Research Grant Council of Hong Kong.     

半定优化与半光滑牛顿算法

本文讨论半光滑牛顿算法的基本概念与其在求解半定优化问题中的应用．特别地，该算法可用于求解线性或非线性半定互补问题．本文同时综述最近在矩阵方程，增广拉格朗日公式和半定优化稳定性方面的、源于半光滑牛顿算法的理论成果．     

On fully discrete Galerkin methods of second-order temporal accuracy for the nonlinear Schrödinger equation

Summary We approximate the solutions of an initial- and boundary-value problem for nonlinear Schrödinger equations (with emphasis on the cubic nonlinearity) by two fully discrete finite element schemes based on the standard Galerkin method in space and two implicit. Crank-Nicolson-type second-order accurate temporal discretizations. For both schemes we study the existence and uniqueness of their solutions and proveL ² error bounds of optimal order of accuracy. For one of the schemes we also analyze one step of Newton's method for solving the nonlinear systems that arise at every time step. We then implement this scheme using an iterative modification of Newton's method that, at each time stept ⁿ , requires solving a number of sparse complex linear systems with a matrix that does not change withn. The effect of this inner iteration is studied theoretically and numerically.The work of these authors was supported by the Institute of Applied and Computational Mathematics of the Research Center of Crete-FORTH and the Science Alliance program of the University of TennesseeThe work of this author was supported by the AFOSR Grant 88-0019     

Nonlinear leastpth optimization and nonlinear programming

Over the past few years a number of researchers in mathematical programming became very interested in the method of the Augmented Lagrangian to solve the nonlinear programming problem. The main reason being that the Augmented Lagrangian approach overcomes the ill-conditioning problem and the slow convergence of the penalty methods. The purpose of this paper is to present a new method of solving the nonlinear programming problem, which has similar characteristics to the Augmented Lagrangian method. The original nonlinear programming problem is transformed into the minimization of a leastpth objective function which under certain conditions has the same optimum as the original problem. Convergence and rate of convergence of the new method is also proved. Furthermore numerical results are presented which illustrate the usefulness of the new approach to nonlinear programming.This work was supported by the National Research Council of Canada and by the Department of Combinatorics and Optimization of the University of Waterloo.     

Exact augmented Lagrangian functions for nonlinear semidefinite programming

In this paper, we study augmented Lagrangian functions for nonlinear semidefinite programming (NSDP) problems with exactness properties. The term exact is used in the sense that the penalty parameter can be taken appropriately, so a single minimization of the augmented Lagrangian recovers a solution of the original problem. This leads to reformulations of NSDP problems into unconstrained nonlinear programming ones. Here, we first establish a unified framework for constructing these exact functions, generalizing Di Pillo and Lucidi’s work from 1996, that was aimed at solving nonlinear programming problems. Then, through our framework, we propose a practical augmented Lagrangian function for NSDP, proving that it is continuously differentiable and exact under the so-called nondegeneracy condition. We also present some preliminary numerical experiments.     

An augmented Lagrangian technique for variational inequalities

A general framework for the treatment of a class of elliptic variational inequalities by an augmented Lagrangian method, when inequalities with infinite-dimensional image space are augmented, is developed. Applications to the obstacle problem, the elastoplastic torsion problem, and the Signorini problem are given.The research of the first author was supported in part by the Air Force Office of Scientific Research under Grants AFOSR-84-0398 and AFOSR-85-0303, by the National Aeronautics and Space Administration under Grant NAG-1-1517, and by NSF under Grant No. UINT-8521208. The second author's research was supported in part by the Fonds zur Förderung der wissenschaftlichen Forschung under S3206 and P6005.