Temporal boundary value problems in interfacial fluid dynamics

We study problems in interfacial fluid dynamics which do not have well-posed initial value problems. We prove existence of solutions for these problems by considering instead boundary value problems, where boundary data is specified at two different times. We develop a general framework, for problems on the real line and for problems which are spatially periodic. A variety of boundary conditions are considered, including Dirichlet, Neumann and mixed conditions. The framework is applied to two specific problems from interfacial fluid dynamics: a family of generalizations of the Boussinesq equations developed by Bona, Chen and Saut, and the vortex sheet.     

Differential equations having rapidly changing solutions: Analytic methods for weakly nonlinear systems

Initial value problems for weakly nonlinear systems of differential equations are studied here. A perturbation result is obtained which is shown to give a valid approximation to the solution. The approximation is determined by solving two associated problems, and it is especially derived for problems which have rapidly oscillating and rapidly decaying solutions.     

Fuzzy matrix games via a fuzzy relation approach

A generalized model for a two person zero sum matrix game with fuzzy goals and fuzzy payoffs via fuzzy relation approach is introduced, and it is shown to be equivalent to two semi-infinite optimization problems. Further, in certain special cases, it is observed that the two semi-infinite optimization problems reduce to (finite) linear programming problems which are dual to each other either in the fuzzy sense or in the crisp sense.     

A Unifying Approach to Variational Relation Problems

The purpose of this paper is to present a unified approach to study the existence of solutions for two types of variational relation problems, which encompass several generalized equilibrium problems, variational inequalities and variational inclusions investigated in the recent literature. By using two well-known fixed point theorems, we establish several existence criteria for the solutions of these problems.     

A NEW STEPSIZE FOR THE STEEPEST DESCENT METHOD

The steepest descent method is the simplest gradient method for optimization. It is well known that exact line searches along each steepest descent direction may converge very slowly. An important result was given by Barzilar and Borwein, which is proved to be superlinearly convergent for convex quadratic in two dimensional space, and performs quite well for high dimensional problems. The BB method is not monotone, thus it is not easy to be generalized for general nonlinear functions unless certain non-monotone techniques being applied. Therefore, it is very desirable to find stepsize formulae which enable fast convergence and possess the monotone property. Such a stepsize αk for the steepest descent method is suggested in this paper. An algorithm with this new stepsize in even iterations and exact line search in odd iterations is proposed. Numerical results are presented, which confirm that the new method can find the exact solution within 3 iteration for two dimensional problems. The new method is very efficient for small scale problems. A modified version of the new method is also presented, where the new technique for selecting the stepsize is used after every two exact line searches. The modified algorithm is comparable to the Barzilar-Borwein method for large scale problems and better for small scale problems.     

Inverse Problem for a Fourth-Order Differential Operator with Nonseparated Boundary Conditions

Uniqueness theorems are proved for two inverse problems for a fourth-order differential operator with nonseparated boundary conditions. The first of the problems, which has technical applications, is the problem of identification of a differential equation and two boundary conditions, and the second problem is the problem of identification of a differential equation and four boundary conditions. One of two data sets is used as the spectral data of the problem. The first data set is the spectrum of the problem itself (or three of its eigenvalues) and the spectral data of a system of three problems, and the second data set is the spectrum of the problem itself (or three of its eigenvalues) and the spectra of ten boundary value problems.     

Convergence and Application of a Decomposition Method Using Duality Bounds for Nonconvex Global Optimization

The subject of this article is a class of global optimization problems, in which the variables can be divided into two groups such that, in each group, the functions involved have the same structure (e.g. linear, convex or concave, etc.). Based on the decomposition idea of Benders (Ref. 1), a corresponding master problem is defined on the space of one of the two groups of variables. The objective function of this master problem is in fact the optimal value function of a nonlinear parametric optimization problem. To solve the resulting master problem, a branch-and-bound scheme is proposed, in which the estimation of the lower bounds is performed by applying the well-known weak duality theorem in Lagrange duality. The results of this article concentrate on two subjects: investigating the convergence of the general algorithm and solving dual problems of some special classes of nonconvex optimization problems. Based on results in sensitivity and stability theory and in parametric optimization, conditions for the convergence are established by investigating the so-called dual properness property and the upper semicontinuity of the objective function of the master problem. The general algorithm is then discussed in detail for some nonconvex problems including concave minimization problems with a special structure, general quadratic problems, optimization problems on the efficient set, and linear multiplicative programming problems.     

Existence of an extremal ground state energy of a nanostructured quantum dot

This paper is concerned with two rearrangement optimization problems. These problems are motivated by two eigenvalue problems which depend nonlinearly on the eigenvalues. We consider a rational and a quadratic eigenvalue problem with Dirichlet’s boundary condition and investigate two related optimization problems where the goal function is the corresponding first eigenvalue. The first eigenvalue in the rational eigenvalue problem represents the ground state energy of a nanostructured quantum dot. In both the problems, the admissible set is a rearrangement class of a given function.     

Numerical aspects of monotone approximations in convex stochastic control problems

The paper concerns a numerical study of the performance of an approximate algorithm for convex stochastic control problems that was derived in a recent paper by two of the authors and is inspired by work done in the area of Stochastic Programming in connection with the Edmundson-Madansky inequality. The accuracy of the approximations is numerically tested by applying the algorithm to two classical convex stochastic control problems for which the optimal solution is known, namely the linear-quadratic and the inventory control problems.     

A quantile domain perspective on the relationships between optimal grouping, spacing and stratification problems

The relationships between two distributions having the same solutions for problems of optimal spacing selection for the asymptotically best linear unbiased estimator of a location or scale parameter or for problems of optimal stratification for estimation of a population mean are investigated. Easily checked necessary and sufficient conditions under which two distributions have identical solutions to these problems are given in terms of their quantile and density-quantile functions. As an application of these results a quantile domain analoque of a theorem due to Adatia and Chan (1981) on the equivalence of optimal grouping, spacing and stratification problems is obtained.     

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.     

Solution of Set-Covering and Set-Partitioning Problems Using Assignment Relaxations

Graph theoretic relaxations are used to design tree search algorithms for set-covering and set-partitioning problems. In this paper two assignment relaxations for the set-covering and set-partitioning problems are presented and a tree search method is developed which makes use of these relaxations. Computational experience of processing a collection of test problems is reported.     

三维弹塑性有摩擦接触问题求解的一个新算法

空间弹塑性有摩擦问题是两种非线性相互耦合的边值待定问题,由于涉及到空间接触问题,切向滑动状态的确定就成为一个重要的方面,为获得高精度的结果,大量的计算工作是难免的.规划法与迭代法是处理这类问题的两种重要的方法.作者提出了将这两种方法相结合的规划-迭代算法,充分利用两种方法各自的长处来处理空间弹塑性接触问题.数值结果表明了所提出算法的良好性态.     

Optimal subgradient algorithms for large-scale convex optimization in simple domains

This paper describes two optimal subgradient algorithms for solving structured large-scale convex constrained optimization. More specifically, the first algorithm is optimal for smooth problems with Lipschitz continuous gradients and for Lipschitz continuous nonsmooth problems, and the second algorithm is optimal for Lipschitz continuous nonsmooth problems. In addition, we consider two classes of problems: (i) a convex objective with a simple closed convex domain, where the orthogonal projection onto this feasible domain is efficiently available; and (ii) a convex objective with a simple convex functional constraint. If we equip our algorithms with an appropriate prox-function, then the associated subproblem can be solved either in a closed form or by a simple iterative scheme, which is especially important for large-scale problems. We report numerical results for some applications to show the efficiency of the proposed schemes.     

A Set of Greedy Randomized Adaptive Local Search Procedure (GRASP) Implementations for the Multidimensional Assignment Problem

The focal problem for centralized multisensor multitarget tracking is the data association problem of partitioning the observations into tracks and false alarms so that an accurate estimate of the true tracks can be recovered. Large classes of these association problems can be formulated as multidimensional assignment problems, which are known to be NP-hard for three dimensions or more. The assignment problems that result from tracking are large scale, sparse and noisy. Solution methods must execute in real-time. The Greedy Randomized Adaptive Local Search Procedure (GRASP) has proven highly effective for solving many classes NP-hard optimization problems. This paper introduces four GRASP implementations for the multidimensional assignment problem, which are combinations of two constructive methods (randomized reduced cost greedy and randomized max regret) and two local search methods (two-assignment-exchange and variable depth exchange). Numerical results are shown for a two random problem classes and one tracking problem class.     

Structural schedulings in the problems of scheduling theory

This paper is devoted to a class of problems for which there exist optimal structure schedulings. One presents the solutions of two problems of scheduling theory. One analyzes the similarities and the differences in the formulation between the problems of this class and the traditional problems of scheduling theory.     

Optimal stopping for non-linear expectations—Part I

We develop a theory for solving continuous time optimal stopping problems for non-linear expectations. Our motivation is to consider problems in which the stopper uses risk measures to evaluate future rewards. Our development is presented in two parts. In the first part, we will develop the stochastic analysis tools that will be essential in solving the optimal stopping problems, which will be presented in Bayraktar and Yao (2011) [1].     

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.     

Packing problems and project scheduling models: an integrating perspective

This paper considers two problem classes, namely packing and project scheduling problems that are important to researchers as well as practitioners. The two problem categories are described, and a classification is given for the different kinds of packing problems and project scheduling concepts. While both problem classes are different with respect to their fields of application, similarities of their mathematical structures are examined. It is shown that all packing problems considered here are special cases of models for project scheduling. The aim is to indicate which project scheduling models can be used to capture the different types of packing problems. Finally, some implications for research on optimisation algorithms for these two problem classes are discussed, and the applicability of the results of this work in practice are pointed out.     

A modified combined relaxation method for non-linear convex variational inequalities

We consider a class of non-linear problems which is intermediate between equilibrium and variational inequality ones and has many applications. Unlike the usual variational inequality it involves two non-linear mappings, which need not be differentiable. We propose a class of iterative methods for this problem, which converge to a solution under weakened monotonicity type assumptions. This method is simpler essentially in comparison with those for the corresponding non-linear equilibrium problems.