A generalisation of the interval Newton single-step method for nonlinear systems of equations

In this paper interval iteration methods for solving large nonlinear systems of equations are considered. Already well-known methods are combined to a new one, whose enclosing properties are better than those of previous methods. Convergence of this new method is shown, based on a new convergence proof for the interval Newton single-step method. The central concept in this case is the fixpoint inverse of an interval matrix. Practical tests with nonlinear systems of equations arising from discretisation of certain elliptic partial differential equations show the efficiency of the new method.     

Some remarks on two interval-arithmetic modifications of the newton method

In this paper we consider the interval Newton method and its simplified version to find tight bounds for the solutions of systems of nonlinear equations. Referring to a paper of Alefeld on this subject, we derive a connection between the two matrices used there to formulate sufficient criteria for the convergence of these methods. We also answer an open question concerning the break down of the Newton method after a finite number of steps.     

Observer design for nonlinear systems with discrete-timemeasurements

This paper focuses on the development of asymptotic observers for nonlinear discrete-time systems. It is argued that instead of trying to imitate the linear observer theory, the problem of constructing a nonlinear observer can be more fruitfully studied in the context of solving simultaneous nonlinear equations. In particular, it is shown that the discrete Newton method, properly interpreted, yields an asymptotic observer for a large class of discrete-time systems, while the continuous Newton method may be employed to obtain a global observer. Furthermore, it is analyzed how the use of Broyden's method in the observer structure affects the observer's performance and its computational complexity. An example illustrates some aspects of the proposed methods; moreover, it serves to show that these methods apply equally well to discrete-time systems and to continuous-time systems with sampled outputs     

Optimally Convergent HDG Method for Third-Order Korteweg–de Vries Type Equations

We develop and analyze a new hybridizable discontinuous Galerkin method for solving third-order Korteweg–de Vries type equations. The approximate solutions are defined by a discrete version of a characterization of the exact solution in terms of the solutions to local problems on each element which are patched together through transmission conditions on element interfaces. We prove that the semi-discrete scheme is stable with proper choices of stabilization function in the numerical traces. For the linearized equation, we carry out error analysis and show that the approximations to the exact solution and its derivatives have optimal convergence rates. In numerical experiments, we use an implicit scheme for time discretization and the Newton–Raphson method for solving systems of nonlinear equations, and observe optimal convergence rates for both the linear and the nonlinear third-order equations.     

Interval Mathematical Library Based on Chebyshev and Taylor Series Expansion

In this paper, we present a mathematical library designed for use in interval solvers of nonlinear systems of equations. The library computes the validated upper and lower bounds of ranges of values of elementary mathematical functions on an interval, which are optimal in most cases. Computation of elementary functions is based on their expansion in Chebyshev and Taylor series and uses the rounded directions setting mechanism. Some original techniques developed by the authors are applied in order to provide high speed and accuracy of the computation.     

Iterative algorithm to compute the maximal and stabilising solutions of a general class of discrete-time Riccati-type equations

In this article an iterative method to compute the maximal solution and the stabilising solution, respectively, of a wide class of discrete-time nonlinear equations on the linear space of symmetric matrices is proposed. The class of discrete-time nonlinear equations under consideration contains, as special cases, different types of discrete-time Riccati equations involved in various control problems for discrete-time stochastic systems. This article may be viewed as an addendum of the work of Dragan and Morozan (Dragan, V. and Morozan, T. (2009), ‘A Class of Discrete Time Generalized Riccati Equations’, Journal of Difference Equations and Applications, first published on 11 December 2009 (iFirst), doi: 10.1080/10236190802389381) where necessary and sufficient conditions for the existence of the maximal solution and stabilising solution of this kind of discrete-time nonlinear equations are given. The aim of this article is to provide a procedure for numerical computation of the maximal solution and the stabilising solution, respectively, simpler than the method based on the Newton–Kantorovich algorithm.     

Optimizing INTBIS on the CRAY Y-MP

INTBIS is a well-tested software package which uses an interval Newton/generalized bisection method to find all numerical solutions to nonlinear systems of equations. Since INTBIS uses interval computations, its results are guaranteed to contain all solutions. To efficiently solve very large nonlinear systems on a parallel vector computer, it is necessary to effectively utilize the architectural features of the machine In this paper, we report our implementations of INTBIS for large nonlinear systems on the Cray Y-MP supercomputer. We first present the direct implementation of INTBIS on a Cray. Then, we report our work on optimizing INTBIS on the Cray Y-MP     

A Newton method for solving continuous multiple material minimum compliance problems

This paper presents an implementation of an active-set line-search Newton method intended for solving large-scale instances of a class of multiple material minimum compliance problems. The problem is modeled with a convex objective function and linear constraints. At each iteration of the Newton method, one or two linear saddle point systems are solved. These systems involve the Hessian of the objective function, which is both expensive to compute and completely dense. Therefore, the linear algebra is arranged such that the Hessian is not explicitly formed. The main concern is to solve a sequence of closely related problems appearing as the continuous relaxations in a nonlinear branch and bound framework for solving discrete minimum compliance problems. A test-set consisting of eight discrete instances originating from the design of laminated composite structures is presented. Computational experiments with a branch and bound method indicate that the proposed Newton method can, on most instances in the test-set, take advantage of the available starting point information in an enumeration tree and resolve the relaxations after branching with few additional function evaluations. Discrete feasible designs are obtained by a rounding heuristic. Designs with provably good objective functions are presented.     

An adaptive nonmonotone truncated Newton method for optimal control of a class of parabolic distributed parameter systems

A black-box method using the finite elements, the Crank–Nicolson and a nonmonotone truncated Newton (TN) method is presented for solving optimal control problems (OCPs) governed by partial differential equations (PDEs). The proposed method finds the optimal control of a class of linear and nonlinear parabolic distributed parameter systems with a quadratic cost functional. To this end, the piecewise linear finite elements method and the well-known Crank–Nicolson method are used for discretizing in space and in time, respectively. Afterwards, regarding the implicit function theorem (IFT), the optimal control problem is transformed into an unconstrained nonlinear optimization problem. Considering that in a gradient-based method for solving optimal control problems, the evaluations of gradients and Hessians of the cost functional is important, hence, an adjoint technique is used to evaluate them effectively. In addition, to make a globalization strategy, we first introduce an adaptive nonmonotone strategy which properly controls the degree of nonmonotonicity and then incorporate it into an inexact Armijo-type line search approach to construct a more relaxed line search procedure. Finally, the obtained unconstrained nonlinear optimization problem is solved by utilizing the proposed nonmonotone truncated Newton method. Results gained from the new offered method compared with existing methods show that the new method is promising.     

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.     

Study of a Jacobian-free approach in the simulation of compressible fluid flows in porous media using a derivative-free spectral method

The development of Jacobian-free software for solving problems formulated by nonlinear partial differential equations is of increasing interest to simulate practical engineering processes. For the first time, this work uses the so-called derivative-free spectral algorithm for nonlinear equations in the simulation of flows in porous media. The model considered here is the one employed to describe the displacement of miscible compressible fluid in porous media with point sources and sinks, where the density of the fluid mixture varies exponentially with the pressure. This spectral algorithm is a modern method for solving large-scale nonlinear systems, which does not use any explicit information associated with the Jacobin matrix of the considered system, being a Jacobian-free approach. Two dimensional problems are presented, along with numerical results comparing the spectral algorithm to a well-developed Jacobian-free inexact Newton method. The results of this paper show that this modern spectral algorithm is a reliable and efficient method for simulation of compressible flows in porous media.     

A Regularized Semi-Smooth Newton Method with Projection Steps for Composite Convex Programs

The goal of this paper is to study approaches to bridge the gap between first-order and second-order type methods for composite convex programs. Our key observations are: (1) Many well-known operator splitting methods, such as forward–backward splitting and Douglas–Rachford splitting, actually define a fixed-point mapping; (2) The optimal solutions of the composite convex program and the solutions of a system of nonlinear equations derived from the fixed-point mapping are equivalent. Solving this kind of system of nonlinear equations enables us to develop second-order type methods. These nonlinear equations may be non-differentiable, but they are often semi-smooth and their generalized Jacobian matrix is positive semidefinite due to monotonicity. By combining with a regularization approach and a known hyperplane projection technique, we propose an adaptive semi-smooth Newton method and establish its convergence to global optimality. Preliminary numerical results on \(\ell _1\)-minimization problems demonstrate that our second-order type algorithms are able to achieve superlinear or quadratic convergence.     

A New Technique in Systems Analysis Under Interval Uncertainty and Ambiguity

The main subject of this work is mathematical and computational aspects of modeling of static systems under interval uncertainty and/or ambiguity. A cornerstone of the new approach we are advancing in the present paper is, first, the rigorous and consistent use of the logical quantifiers to characterize and distinguish different kinds of interval uncertainty that occur in the course of modeling, and, second, the systematic use of Kaucher complete interval arithmetic for the solution of problems that are minimax by their nature. As a formalization of the mathematical problem statement, concepts of generalized solution sets and AE-solution sets to an interval system of equations, inequalities, etc., are introduced. The major practical result of our paper is the development of a number of techniques for inner and outer estimation of the so-called AE-solution sets to interval systems of equations. We work out, among others, formal approach, generalized interval Gauss-Seidel iteration, generalized preconditioning and PPS-methods. Along with the general nonlinear case, the linear systems are treated more thoroughly.     

A non-interior implicit smoothing approach to complementarity problems for frictionless contacts

This paper presents a non-interior point method for solving frictionless contact problems in large deformations, where we solve the problems in an incremental path-following method from warm start. We propose a novel reformulation of the nonlinear complementarity problem, which is based on the smoothed Fischer–Burmeister function but is distinguished from the conventional formulations in the following two particular aspects: (i) the smoothing parameter is considered as an independent variable; (ii) an equality constraint is added so that the smoothing parameter serves as a measure of the residual of the complementarity conditions. The reduced system of nonlinear equations is solved with a conventional Newton method for nonlinear equations from the initial point which is defined by using the solution of the preceding loading stage. Throughout numerical examples it is shown that in many cases the solution can be found within four Newton iterations.     

Parallel Solutions for Large-Scale General Sparse Nonlinear Systems of Equations

In solving application problems,many large-scale nonlinear systems of equaions result in sparse Jacobian matrices.Such nonlinear systems are called sparse nonlinear systems.The irregularity of the locations of nonzrero elements of a general sparse matrix makes it very difficult to generally map sparse matrix computations to multiprocessors for parallel processing in a well balanced manner.To overcome this difficulty,we define a new storage scheme for general sparse matrices in this paper,With the new storage scheme,we develop parallel algorithms to solve large-scale general sparse systems of equations by interval Newton/Generalized bisection methods which reliably find all numerical solutions within a given domain.I n Section 1,we provide an introduction to the addressed problem and the interval Newton‘s methods.In Section 2,some currently used storage schemes for sparse systems are reviewed.In Section 3,new index schemes to store general sparse matrices are reported.In Section 4,we present a parallel algorithm to evaluate a general sparse Jacobian matrix.In Section 5,we present a parallel algorithm to solve the corresponding interval linear system by the all-row preconditioned scheme.Conclusions and future work are discussed in Section 6.     

Finite element approximation of nonlinear transient magnetic problems involving periodic potential drop excitations

This paper deals with the computation of nonlinear 2D transient magnetic fields when the data concerning the electric current sources involve potential drop excitations. In the first part, a mathematical model is stated, which is solved by an implicit time discretization scheme combined with a finite element method for space approximation. The second part focuses on developing a numerical method to compute periodic solutions by determining a suitable initial current which avoids large simulations to reach the steady state. This numerical method leads to solve a nonlinear system of equations which requires to approximate several nonlinear and linear magnetostatic problems. The proposed methods are first validated with an axisymmetric example and sinusoidal source having an analytical solution. Then, we show the saving in computational effort that this methodology offers to approximate practical problems specially with pulse-width modulation (PWM) voltage supply.     

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。     

Newton-Type method for a class of mathematical programs with complementarity constraints

In this paper, we present a Newton-type method for a class of mathematical programs with complementarity constraints. Under the MPEC-LICQ, we use the definition of B-stationary point to construct a constrained equations model, and apply the Newton method to solve the problem. At the end of this paper, numerical results are reported to show our method's validity.     

A combined method for enclosing all solutions of nonlinear systems of polynomial equations

We consider the problem of finding interval enclosures of all zeros of a nonlinear system of polynomial equations. We present a method which combines the method of Gröbner bases (used as a preprocessing step), some techniques from interval analysis, and a special version of the algorithm of E. Hansen for solving nonlinear equations in one variable. The latter is applied to a triangular form of the system of equations, which is generated by the preprocessing step. Our method is able to check if the given system has a finite number of zeros and to compute verfied enclosures for all these zeros. Several test results demonstrate that our method is much faster than the application of Hansen’s multidimensional algorithm (or similar methods) to the original nonlinear systems of polynomial equations.     

求解一类凸优化问题的区间迭代算法

研究了一类非线性带约束的凸优化问题的求解.利用Kuhn-Tucker条件将凸优化问题等价地转化为多变元非线性方程组的求解问题.基于区间算术的包含原理及改进的Krawczyk区间迭代算法,提出一个求解凸优化问题的区间算法.对于目标函数和约束函数可微的凸优化,所提算法具有全局寻优的特性.在数值实验方面,与遗传算法、模式搜索法、模拟退火法及数学软件内置的求解器进行了比较,结果表明所提算法就此类凸优化问题能找到较多且误差较小的全局最优点.