Nonoscillation theorems in convex sets

This paper presents a new and simple technique for a certain class of variational problems which includes many of the important problems of mathematical physics, e.g., the Brachistochrone, geodesies, and minimal surface of revolution problems. The technique uses Caratheodory's equivalent problems approach but combines two equivalent problems at the same time to get the sufficiency and uniqueness results. It does not use any of the classical sufficiency conditions such as the Weierstrass condition. The equations that we are led to by this new approach turn out to be the Hamilton-Jacobi and Euler-Lagrange equations for the problem, but here we have not had to use any of the classical Hamilton-Jacobi theory nor derivations to get its results (e.g., orthogonality of the extremals to the wave fronts) for this class of problems. The cases for one and n dependent variables are presented and illustrated. Implications and generalizations of the method are discussed.     

An infinite element theory

A technique for solution of plane problems in mathematical physics or mechanics is presented. The method was developed initially for elastic plate problems with transverse load concentrations but is here extended to other two-dimensional problems. Draw-down problems with a number of wells in a porous medium, heat-flow problems in a plate with a distributed source, and with discrete sinks, or membrane displacements under concentrated forces, are considered in particular.The technique is to treat the two-dimensional medium as infinite: each source or sink is treated separately and equilibrated by an auxiliary source function. Superposition allows the effects of the separate sources and sinks to be added. The residual effect of the auxiliary source, after superposition, is negligible so that the summed effects of the individual sources and sinks gives the required solution. The method is approximate but the error may be made arbitrarily small. It is simple and well suited to the class of problems above, and to either manual or computer based analyses. Examples of its use are given.     

On the stability of the solution mapping for parametric traffic network problems

In this paper, we introduce the parametric traffic network problems. Afterward, a key hypothesis is introduced by virtue of a parametric gap function to considered problems, and we prove that this hypothesis is not only sufficient but also necessary for the Hausdorff lower semicontinuity and Hausdorff continuity of the solution mapping for parametric traffic network problems.     

逆向思维的形式化模型及其应用

逆向思维是一种解决矛盾问题和创新问题的重要思维模式,但目前大部分学者只是运用自然语言对其进行定性研究.利用可拓学的形式化工具——基元和可拓变换,给出了逆向变换和逆向基元的定义,从而构造了逆向思维的形式化模型,这为将来按照一定程序,甚至利用计算机软件生成逆向思维策略解决矛盾问题和创新问题奠定了基础,对于矛盾问题和创新问题的智能化处理研究具有不可替代的意义.     

System dynamics in defence analysis: some case studies

System dynamics has proved to be an effective and efficient methodology for the analysis of defence problems. The paper first reviews some academic background to the defence applications of system dynamics, that is followed by a brief account of two case studies which illustrate the very wide variety of problems in this domain. Not only are the problems different but their analysis illustrates two aspects of system dynamics. Firstly its role is traditional quantitative modelling but it is shown that this is enhanced when system dynamics is combined with other methodologies, and secondly it shows system dynamics' scope for shedding light by purely qualitative, diagrammatic, analysis. However, defence is not solely a matter of the conduct of military operations of different types but also includes the special problems of finance and procurement. Work in that area is dealt with by a brief account of two applications. Finally, the reasons for the success of system dynamics in this domain are considered.     

Design of software for ODEs

We discuss the design of software that is easy to use for simple problems, but still capable of solving complicated problems. Nowadays a design should accommodate related, but fundamentally different, tasks such as solving DAEs and DDEs in addition to IVPs and BVPs for ODEs. Major issues are illustrated with the MATLAB ODE Suite and selected solvers written in Fortran 90 and Maple.     

On the quadratic two-parameter eigenvalue problem and its linearization

We introduce the quadratic two-parameter eigenvalue problem and linearize it as a singular two-parameter eigenvalue problem. This, together with an example from model updating, shows the need for numerical methods for singular two-parameter eigenvalue problems and for a better understanding of such problems.There are various numerical methods for two-parameter eigenvalue problems, but only few for nonsingular ones. We present a method that can be applied to singular two-parameter eigenvalue problems including the linearization of the quadratic two-parameter eigenvalue problem. It is based on the staircase algorithm for the extraction of the common regular part of two singular matrix pencils.     

Reliable Numerical Computation in Civil Engineering

Civil engineering is a field – as are many other engineering sciences – where most of the methods used for solving optimization problems are based on experience and experiments, and models using local information, but drawn from global models. The present work outlines an interesting class of problems from this field, and initiates some possible ways to solve those problems utilizing the wide tool capabilities of interval arithmetic for error handling and interval branch-and-bound algorithms to solve the original or modified industrial models automating civil engineers' work. The investigations are in the first state but are promising both in a theoretical and in a practical sense.     

The numerical solution of unstable two point boundary value problems

A number of workers have tried to solve, numerically, unstable two point boundary value problems. Multiple Shooting and Continuation Methods have been used very successfully for these problems, but each has weaknesses; for particularly unstable problems their success may be partial. In this paper we develop an algorithm that attempts to solve these problems in a routine manner.The algorithm uses a combination of Multiple Shooting and Range Extension in such a way that the advantages of both are maintained while the effects of their disadvantages are reduced considerably. The success of the algorithm is demonstrated on some particularly unstable problems.     

Problem solving in the borderland between mathematics and physics

The article addresses the problématique of where mathematization is taught in the educational system, and who teaches it. Mathematization is usually not a part of mathematics programs at the upper secondary level, but we argue that physics teaching has something to offer in this respect, if it focuses on solving so-called unformalized problems, where a major challenge is to formalize the problems in mathematics and physics terms. We analyse four concrete examples of unformalized problems for which the formalization involves different order of mathematization and applying physics to the problem, but all require mathematization. The analysis leads to the formulation of a model by which we attempt to capture the important steps of the process of solving unformalized problems by means of mathematization and physicalization.     

From the 1-2-3 conjecture to the Riemann hypothesis

This survey presents some combinatorial problems with number-theoretic flavor. Our journey starts from a simple graph coloring question, but at some point gets close to dangerous territory of the Riemann Hypothesis. We will mostly focus on open problems, but we will also provide some simple proofs, just for adorning.     

空间轴对称问题边界元法的程序处理

弹性力学的空间轴对称问题可以化为两个变量(r,z)的二维问题求解,但又比平面问题略复杂些.在轴对称问题边界元的程序处理上也会相应带来些麻烦.文章具体介绍了作者在调试轴对称问题边界元程序中遇到的一些问题及处理方法,并给出数值结果加以验证.     

A TRULY GLOBALLY CONVERGENT FEASIBLE NEWTON-TYPE METHOD FOR MIXED COMPLEMENTARITY PROBLEMS

Typical solution methods for solving mixed complementarity problems either generatefeasible iterates but have to solve relatively complicated subproblems such as quadraticprograms or linear complementarity problems,or(those methods)have relatively simplesubproblems such as system of linear equations but possibly generate infeasible iterates.In this paper,we propose a new Newton-type method for solving monotone mixed com-plementarity problems,which ensures to generate feasible iterates,and only has to solve asystem of well-conditioned linear equations with reduced dimension per iteration.Withoutany regularity assumption,we prove that the whole sequence of iterates converges to a so-lution of the problem(truly globally convergent).Furthermore,under suitable conditions,the local superlinear rate of convergence is also established.     

A Stochastic/Perturbation Global Optimization Algorithm for Distance Geometry Problems

We present a new global optimization approach for solving exactly or inexactly constrained distance geometry problems. Distance geometry problems are concerned with determining spatial structures from measurements of internal distances. They arise in the structural interpretation of nuclear magnetic resonance data and in the prediction of protein structure. These problems can be naturally formulated as global optimization problems which generally are large and difficult. The global optimization method that we present is related to our previous stochastic/perturbation global optimization methods for finding minimum energy configurations, but has several key differences that are important to its success. Our computational results show that the method readily solves a set of artificial problems introduced by Moré and Wu that have up to 343 atoms. On a set of considerably more difficult protein fragment problems introduced by Hendrickson, the method solves all the problems with up to 377 atoms exactly, and finds nearly exact solution for all the remaining problems which have up to 777 atoms. These preliminary results indicate that this approach has very good promise for helping to solve distance geometry problems.     

Optimal value bounds in nonlinear programming with interval data

We consider nonlinear programming problems the input data of which are not fixed, but vary in some real compact intervals. The aim of this paper is to determine bounds of the optimal values. We propose a general framework for solving such problems. Under some assumption, the exact lower and upper bounds are computable by using two non-interval optimization problems. While these two optimization problems are hard to solve in general, we show that for some particular subclasses they can be reduced to easy problems. Subclasses that are considered are convex quadratic programming and posynomial geometric programming.     

Problems and conjectures concerning connectivity, paths, trees and cycles in tournament-like digraphs

In this paper we collect a substantial number of challenging open problems and conjectures on connectivity, paths, trees and cycles in tournaments and classes of digraphs which contain tournaments as a subclass. The list is by no means exhaustive but is meant to show that the area has a large number of interesting open problems. We also mention problems for general digraphs when they are relevant in the context.     

Parallel interior-point solver for structured quadratic programs: Application to financial planning problems

Many practical large-scale optimization problems are not only sparse, but also display some form of block-structure such as primal or dual block angular structure. Often these structures are nested: each block of the coarse top level structure is block-structured itself. Problems with these characteristics appear frequently in stochastic programming but also in other areas such as telecommunication network modelling. We present a linear algebra library tailored for problems with such structure that is used inside an interior point solver for convex quadratic programming problems. Due to its object-oriented design it can be used to exploit virtually any nested block structure arising in practical problems, eliminating the need for highly specialised linear algebra modules needing to be written for every type of problem separately. Through a careful implementation we achieve almost automatic parallelisation of the linear algebra. The efficiency of the approach is illustrated on several problems arising in the financial planning, namely in the asset and liability management. The problems are modelled as multistage decision processes and by nature lead to nested block-structured problems. By taking the variance of the random variables into account the problems become non-separable quadratic programs. A reformulation of the problem is proposed which reduces density of matrices involved and by these means significantly simplifies its solution by an interior point method. The object-oriented parallel solver achieves high efficiency by careful exploitation of the block sparsity of these problems. As a result a problem with over 50 million decision variables is solved in just over 2 hours on a parallel computer with 16 processors. The approach is by nature scalable and the parallel implementation achieves nearly perfect speed-ups on a range of problems. Supported by the Engineering and Physical Sciences Research Council of UK, EPSRC grant GR/R99683/01     

Extragradient algorithms extended to equilibrium problems¶

We make use of the auxiliary problem principle to develop iterative algorithms for solving equilibrium problems. The first one is an extension of the extragradient algorithm to equilibrium problems. In this algorithm the equilibrium bifunction is not required to satisfy any monotonicity property, but it must satisfy a certain Lipschitz-type condition. To avoid this requirement we propose linesearch procedures commonly used in variational inequalities to obtain projection-type algorithms for solving equilibrium problems. Applications to mixed variational inequalities are discussed. A special class of equilibrium problems is investigated and some preliminary computational results are reported.     

Convergence of algorithms for perturbed optimization problems

Infinite-dimensional optimization problems occur in various applications such as optimal control problems and parameter identification problems. If these problems are solved numerically the methods require a discretization which can be viewed as a perturbation of the data of the optimization problem. In this case the expected convergence behavior of the numerical method used to solve the problem does not only depend on the discretized problem but also on the original one. Algorithms which are analyzed include the gradient projection method, conditional gradient method, Newton's method and quasi-Newton methods for unconstrained and constrained problems with simple constraints.     

Fehlerabschätzungen bei Randwertaufgaben partieller Differentialgleichungen mit unendlichem Grundgebiet

Summary Types of boundary value problems of partial differential equations for infinite domains are discussed which can easily be transformed in such a manner as to allow estimations of error (for approximate solutions) similar to the boundary maximum principle. First, second und third boundary value problems for the outer domain of linear elliptic and certain linear and nonlinear parabolic differential equations are examined. For elliptic differential equations one of the results is that the secound boundary value problem for more than two dimensions can be included. The estimates of the paper can thus be applied to problems of flow around some object, not in the case of two but of three dimensions. This is in a certain sense a counterpart to the conformal mappings method which is successful for two but not for three dimensions. Numerical examples show that estimations of error can easily be carried out.