Error Estimates for the Numerical Approximation of Boundary Semilinear Elliptic Control Problems

We study the numerical approximation of boundary optimal control problems governed by semilinear elliptic partial differential equations with pointwise constraints on the control. The analysis of the approximate control problems is carried out. The uniform convergence of discretized controls to optimal controls is proven under natural assumptions by taking piecewise constant controls. Finally, error estimates are established and some numerical experiments, which confirm the theoretical results, are performed.The first two authors were supported by Ministerio de Ciencia y Tecnología (Spain). The second author was also supported by the DFG research center “Mathematics for key technologies” (FZT86) in Berlin.     

Numerical Approximation of Hysteresis Problems

Hysteresis effects are represented by means of continuous or(multivalued) discontinuous Volterra functionals. Constitutiverelations of this type are coupled with parabolic equationsand existence results are given for the corresponding weak formulations.Implicit time discretization and finite elements are used forthe numerical approximation. Several numerical tests show convergenceof the approximate solutions.     

Dynamic Programming and Inventory Problems

After an introductory discussion of the usefulness of the technique of dynamic programming in solving practical problems of multi-stage decision processes, the paper describes its application to inventory problems. In particular, the effect of allowing the number of decision stages to increase indefinitely is investigated, and it is shown that under certain realistic conditions this situation can be dealt with. It appears to be generally true that the average cost per period will converge, for an optimal policy, as the number of periods considered increases indefinitely, and that it is feasible to search for the policy which minimizes this long-term average cost. The paper concludes with a specific example, in which it is shown that only eight iterations were necessary to find a reasonable approximation to the optimal re-order policy.     

Sequential Quadratic Programming Methods for Large-Scale Problems

Sequential quadratic (SQP) programming methodsare the method of choice when solving small or medium-sized problems. Sincethey are complex methods they are difficult (but not impossible) to adapt tosolve large-scale problems. We start by discussing the difficulties that needto be addressed and then describe some general ideas that may be used toresolve these difficulties. A number of SQP codes have been written to solve specific applications and there is a general purposed SQP code called SNOPT,which is intended for general applications of a particular type. These aredescribed briefly together with the ideas on which they are based. Finally wediscuss new work on developing SQP methods using explicit second derivatives.     

Numerical Approximation and Error Analysis for the Timoshenko Beam Equations with Boundary Feedback

In this paper,the numerical approximation of a Timoshenko beam with bound- ary feedback is considered.We derived a linearized three-level difference scheme on uniform meshes by the method of reduction of order for a Timoshenko beam with boundary feedback.It is proved that the scheme is uniquely solvable,unconditionally stable and second order convergent in L_∞norm by using the discrete energy method. A numerical example is presented to verify the theoretical results.     

基于互惠动机公平均衡的投资项目决策模型及数值模拟分析

针对博弈主体互惠动机公平偏好的证据和特征,本文从理论上描绘了首席执行官和项目经理在信息对称与非对称条件下的资本配置行为和激励合约的设计,在理论模型构建及均衡分析中发现:项目经理互惠动机公平偏好可以提高其努力水平,带来与显性激励合约一样的激励效果,即互惠动机公平偏好产生了“挤进激励效应”;互惠动机公平偏好使得项目经理真实地汇报项目质量状况,优化了公司对项目资本配置的计划,避免了非对称信息下的道德风险问题,即互惠动机公平偏好产生了“信号显示机制”的作用。由于项目经理互惠动机公平偏好的“挤进激励效应”,使得投资项目资本配置决策临界点不会很低,从而抑制了投资不足的严重性。本文的研究结果为具有异质性的项目经理提供了不同合约选择,把“自利”假设下委托代理决策的分析框架拓展到“非自利”的行为委托代理决策的分析框架。     

Sequential Importance Sampling Algorithms for Dynamic Stochastic Programming

This paper gives a comprehensive treatment of EVPI-based sequential importance sampling algorithms for dynamic (multistage) stochastic programming problems. Both theory and computational algorithms are discussed. Under general assumptions it is shown that both an expected value of perfect information (EVPI) process and the corresponding marginal EVPI process (the supremum norm of the conditional expectation of its generalized derivative) are nonanticipative nonnegative supermartingales. These processes are used as importance criteria in the class of sampling algorithms treated in the paper. When their values are negligible at a node of the current sample problem scenario tree, scenarios descending from the node are replaced by a single scenario at the next iteration. On the other hand, high values lead to increasing the number of scenarios descending from the node. Both the small sample and asymptotic properties of the sample problem estimates arising from the algorithms are established, and the former are evaluated numerically in the context of a financial planning problem. Finally, current and future research is described. Bibliography: 49 titles. __________ Published in Zapiski Nauchnykh Seminarov POMI, Vol. 312, 2004, pp. 94–129.     

Analysis of Error Growth Via Stability Regions in Numerical Initial Value Problems

This paper concerns the stability analysis of numerical methods for solving time dependent ordinary and partial differential equations. In the literature stability estimates for such methods were derived, under a condition which can be viewed as a transplantation of the Kreiss resolvent condition (from the unit disk to the stability region S of the numerical method). These estimates tell us that errors in the numerical time stepping process cannot grow faster than linearly with min{s,n}. Here n denotes the number of time steps, and s stands for the order of the (spatial discretization) matrices involved.In this paper we address the natural question of whether the above stability estimates can be improved so as to imply an error growth at a slower rate than min{s,n} (when n, s). Our results concerning this question are as follows: (a) for all (practical) Runge–Kutta and other one-step formulas, we show that the estimates from the literature are sharp in that error growth at the rate min{s,n} can actually occur, (b) for linear multistep formulas we find that, rather surprisingly, some of the stability estimates can substantially be improved and extended, whereas others are sharp.The results proved in this paper are also relevant to (suitably scaled spatial discretization) matrices whose -pseudo-eigenvalues lie at a distance not exceeding K from the stability region S of the time stepping method, for all >0 and fixed constant K.     

二层次消费与投资决策优化动态规划模型

在动态多阶段情形,投资者面临的环境不仅只有投资环境,还包括消费环境.投资者关于投资与消费的决策具有层次性.因为消费事关人的生存需要,是优先要考虑的问题,且投资的最终目的还是为了消费,所以使消费最大化应是高一层次的目标,而使投资最大化则应是次一级的目标.因此,试图建立一个二层次消费与投资决策优化动态规划模型,以便更好地模拟现实世界的情况.讨论了该模型的动态决策过程和最优解的性质.     

Some Theory and an Approach to Solving Sequential Multiple-Criteria Decision Problems

This paper examines a sequential multiple-criteria decision problem. The problem arises when a decision-maker is unable to consider all possible decision alternatives simultaneously. The decision-maker evaluates only a subset of all decision alternatives, from which he chooses the most preferred solution. Obviously, this solution is not necessarily ‘globally’ best. An interesting question is: how good is the most preferred solution and what are the chances of finding a better solution by considering additional alternatives? A unified approach to solving this problem based on probability theory is presented and illustrated with numerical examples.     

An Introduction to Dynamic Programming: The Theory of Multi-Stage Decision Processes

Journal of the Operational Research Society -     

Semilinear Mixed Problems on Hilbert Complexes and Their Numerical Approximation

Arnold, Falk, and Winther recently showed (Bull. Am. Math. Soc. 47:281–354, 2010) that linear, mixed variational problems, and their numerical approximation by mixed finite element methods, can be studied using the powerful, abstract language of Hilbert complexes. In another recent article (arXiv:), we extended the Arnold–Falk–Winther framework by analyzing variational crimes (à la Strang) on Hilbert complexes. In particular, this gave a treatment of finite element exterior calculus on manifolds, generalizing techniques from surface finite element methods and recovering earlier a priori estimates for the Laplace–Beltrami operator on 2- and 3-surfaces, due to Dziuk (Lecture Notes in Math., vol. 1357:142–155, 1988) and later Demlow (SIAM J. Numer. Anal. 47:805–827, 2009), as special cases. In the present article, we extend the Hilbert complex framework in a second distinct direction: to the study of semilinear mixed problems. We do this, first, by introducing an operator-theoretic reformulation of the linear mixed problem, so that the semilinear problem can be expressed as an abstract Hammerstein equation. This allows us to obtain, for semilinear problems, a priori solution estimates and error estimates that reduce to the Arnold–Falk–Winther results in the linear case. We also consider the impact of variational crimes, extending the results of our previous article to these semilinear problems. As an immediate application, this new framework allows for mixed finite element methods to be applied to semilinear problems on surfaces.     

Norm-Based Approximation in Bicriteria Programming

An algorithm to approximate the nondominated set of continuous and discrete bicriteria programs is proposed. The algorithm employs block norms to find an approximation and evaluate its quality. By automatically adapting to the problem's structure and scaling, the approximation is constructed objectively without interaction with the decision maker. Mathematical and practical examples are included.     

Approximation Methods in Multiobjective Programming

Approaches to approximate the efficient set and Pareto set of multiobjective programs are reviewed. Special attention is given to approximating structures, methods generating Pareto points, and approximation quality. The survey covers more than 50 articles published since 1975.His work was supported by Deutsche Forschungsgemeinschaft, Grant HA 1795/7-2.Her work was done while on a sabbatical leave at the University of Kaiserslautern with support of Deutsche Forschungsgemeinschaft, Grant Ka 477/24-1.     

Approximation and Prediction of the Numerical Solution of Some Burgers Problems

The Aitken's ²-prediction of Brezinski has already been used by Morandi Cecchi et al. in order to compute a numerical approximation of the solution of a parabolic initial-boundary value problem. This method consists in two consecutive steps: the first one is the approximation with a finite elements method, where the solution of the involved nonlinear system is computed by Gauss–Seidel method; the second one is a prediction of further terms with Aitken's ²-process. By comparison with this method, we use other methods of prediction in another way. First, we consider a generalization of ²-prediction, the so-called -prediction. In this paper, we only use vector prediction which is more stable than the scalar one. Then, the methods of prediction presented can be used in order to predict the starting vector of the Gauss–Seidel method.     

Error Estimates for the Numerical Approximation of a Semilinear Elliptic Control Problem

We study the numerical approximation of distributed nonlinear optimal control problems governed by semilinear elliptic partial differential equations with pointwise constraints on the control. Our main result are error estimates for optimal controls in the maximum norm. Characterization results are stated for optimal and discretized optimal control. Moreover, the uniform convergence of discretized controls to optimal controls is proven under natural assumptions.