用双向三角级数法解悬臂矩形薄板在均布荷载下的弯曲

悬臂矩形板的弯曲问题是平板理论中的一个难题.多年来,对于这种板只有能量法与数值解法的近似解.1979年以来清华大学张福范教授等用迭加法陆续得出悬臂矩形板在均布荷载和一些集中荷载作用下的解析解.对于在均布荷载作用下的悬臂矩形薄板,本文用双向三角级数法获得了其挠度函数的解析解,并将所得结果与迭加法所得的结果进行了比较.通过比较表明,两种方法计算的结果符合得十分好,因而相互印证了它们的正确性.     

An Additive Problem with Primes in Arithmetic Progressions

In this paper, we extend a classical result of Hua to arithmetic progressions with large moduli. The result implies the Linnik Theorem on the least prime in an arithmetic progression.     

The Special Issue Is Dedicated to Prof Li Tatsien on the Occasion of His 70th Birthday

Professor Li has published more than 200 papers and 16 monographs and textbooks, among which 4 monographs are printed in English in U.S.A., U.K. and France respectively. He has received domestic prizes and awards, including: Shanghai Top Science and Technology Award, Hua Loo-Keng Prize of Mathematics, one Second Prize and one Third Prize of National Natural Sciences from the State, one First Prize of Scientific and Technological Progress from the State Education Commission, and one First Prize of Scientific and Technological Progress from Shanghai Municipality. Two of his works are awarded as Excellent University Textbooks from the State. One Exceptional Shanghai Award for Teaching Achievements in Higher Education and one First National Award for Teaching Achievements in Higher Education. Moreover, he has received the Science and Technology Progress Award of Ho Leug Ho Lee Foundation.     

Optimal nonlinear feedback control of quasi-Hamiltonian systems

An innovative strategy for optimal nonlinear feedback control of linear or nonlinear stochastic dynamic systems is proposed based on the stochastic averaging method for quasi-Hamiltonian systems and stochastic dynamic programming principle. Feedback control forces of a system are divided into conservative parts and dissipative parts. The conservative parts are so selected that the energy distribution in the controlled system is as requested as possible. Then the response of the system with known conservative control forces is reduced to a controlled diffusion process by using the stochastic averaging method. The dissipative parts of control forces are obtained from solving the stochastic dynamic programming equation. Project supported by the National Natural Science Foundation of China (Grant No. 19672054) and Cao Guangbiao High Science and Technology Development Foundation of Zhejiang University.     

Approximating solutions of neutral stochastic evolution equations with jumps

In this paper, we establish existence and uniqueness of the mild solutions to a class of neutral stochastic evolution equations driven by Poisson random measures in some Hilbert space. Moreover, we adopt the Faedo-Galerkin scheme to approximate the solutions. This work was supported by the LPMC at Nankai University and National Natural Science Foundation of China (Grant No. 10671036)     

How does a stochastic optimization/approximation algorithm adapt to a randomly evolving optimum/root with jump Markov sample paths

Stochastic optimization/approximation algorithms are widely used to recursively estimate the optimum of a suitable function or its root under noisy observations when this optimum or root is a constant or evolves randomly according to slowly time-varying continuous sample paths. In comparison, this paper analyzes the asymptotic properties of stochastic optimization/approximation algorithms for recursively estimating the optimum or root when it evolves rapidly with nonsmooth (jump-changing) sample paths. The resulting problem falls into the category of regime-switching stochastic approximation algorithms with two-time scales. Motivated by emerging applications in wireless communications, and system identification, we analyze asymptotic behavior of such algorithms. Our analysis assumes that the noisy observations contain a (nonsmooth) jump process modeled by a discrete-time Markov chain whose transition frequency varies much faster than the adaptation rate of the stochastic optimization algorithm. Using stochastic averaging, we prove convergence of the algorithm. Rate of convergence of the algorithm is obtained via bounds on the estimation errors and diffusion approximations. Remarks on improving the convergence rates through iterate averaging, and limit mean dynamics represented by differential inclusions are also presented. The research of G. Yin was supported in part by the National Science Foundation under DMS-0603287, in part by the National Security Agency under MSPF-068-029, and in part by the National Natural Science Foundation of China under #60574069. The research of C. Ion was supported in part by the Wayne State University Rumble Fellowship. The research of V. Krishnamurthy was supported in part by NSERC (Canada).     

Stochastic vendor selection problem: chance-constrained model and genetic algorithms

We study a vendor selection problem in which the buyer allocates an order quantity for an item among a set of suppliers such that the required aggregate quality, service, and lead time requirements are achieved at minimum cost. Some or all of these characteristics can be stochastic and hence, we treat the aggregate quality and service as uncertain. We develop a class of special chance-constrained programming models and a genetic algorithm is designed for the vendor selection problem. The solution procedure is tested on randomly generated problems and our computational experience is reported. The results demonstrate that the suggested approach could provide managers a promising way for studying the stochastic vendor selection problem. The authors would like to thank the referees for providing constructive comments that led to an improved version of the paper. Also, this research was partially supported by grants from National Natural Science Foundation (60776825)—China, 863 Programs (2007AA11Z208)—China, Doctorate Foundation (20040004012)—China, Villanova University Research Sabbatical Fall 2006, and the National Science Foundation (0332490)—USA.     

On some multi-dimensional quadrature formulas with number-theoretic nets

In the present paper some multi-dimensional quadrature formulas of periodic functions are established by means of the number-theoretic method. Some results of Hua and Wang^[2] are generalized or improved.This project is supported by the National Natural Science Foundation of China.     

中国科大数学系是怎样办学的

中国科学技术大学原副校长、数学系教授龚昇先生2011年1月10日不幸病逝.本刊编委会、编辑部于2011年1月第1期发表《沉痛悼念著名数学家龚？教授》一文,简短介绍了他的生平和学术成就.本期特转载他为《中国科学技术大学数学五十年》一书所作的序,以纪念这位杰出的数学家和数学教育家.本文标题为编者所加.1958年,经中共中央...     

Optimal allocation of policy limits and deductibles under distortion risk measures

In the literature, orderings of optimal allocations of policy limits and deductibles were established by maximizing the expected utility of wealth of the policyholder. In this paper, by applying the bivariate characterizations of stochastic ordering relations, we reconsider the same model and derive some new refined results on orderings of optimal allocations of policy limits and deductibles with respect to the family of distortion risk measures from the viewpoint of the policyholder. Both loss severities and loss frequencies are considered. Special attention is given to the optimization criteria of the family of distortion risk measures with concave distortions and with only increasing distortions. Most of the results presented in this paper can be applied to some particular distortion risk measures. The results complement and extend the main results in Cheung [Cheung, K.C., 2007. Optimal allocation of policy limits and deductibles. Insurance: Mathematics and Economics 41, 291-382] and Hua and Cheung [Hua, L., Cheung, K.C., 2008a. Stochastic orders of scalar products with applications. Insurance: Mathematics and Economics 42, 865-872].     

The value of the stochastic solution in stochastic linear programs with fixed recourse

Stochastic linear programs have been rarely used in practical situations largely because of their complexity. In evaluating these problems without finding the exact solution, a common method has been to find bounds on the expected value of perfect information. In this paper, we consider a different method. We present bounds on the value of the stochastic solution, that is, the potential benefit from solving the stochastic program over solving a deterministic program in which expected values have replaced random parameters. These bounds are calculated by solving smaller programs related to the stochastic recourse problem.This paper is an extension of part of the author's dissertation in the Department of Operations Research, Stanford University, Stanford, California. The research was supported at Stanford by the Department of Energy under Contract DE-AC03-76SF00326, PA#DE-AT03-76ER72018, Office of Naval Research under Contract N00014-75-C-0267 and the National Science Foundation under Grants MCS76-81259, MCS-7926009 and ECS-8012974 (formerly ENG77-06761).     

Computational Methods for Pricing American Put Options

This work develops computational methods for pricing American put options under a Markov-switching diffusion market model. Two methods are suggested in this paper. The first method is a stochastic approximation approach. It can handle option pricing in a finite horizon, which is particularly useful in practice and provides a systematic approach. It does not require calibration of the system parameters nor estimation of the states of the switching process. Asymptotic results of the recursive algorithms are developed. The second method is based on a selling rule for the liquidation of a stock for perpetual options. Numerical results using stochastic approximation and Monte Carlo simulation are reported. Comparisons of different methods are made. This research was supported in part by the National Science Foundation and in part by the Wayne State University Research Enhancement Program.     

Maximum principle of stochastic controlled systems of functional type

This paper studies the optimal controls of stochastic systems of functional type with end constraints. The systems considered may be degenerate and the control region may be nonconvex. A stochastic maximum principle is derived. The method is based on the idea that stochastic systems are essentially infinite dimensional systems. The Project Supported by National Natural Science Fundation of China.     

Asymptotic distributions of M-estimators in a spatial regression model under some fixed and stochastic spatial sampling designs

In this paper, we consider M-estimators of the regression parameter in a spatial multiple linear regression model. We establish consistency and asymptotic normality of the M-estimators when the data-sites are generated by a class of deterministic as well as a class of stochastic spatial sampling schemes. Under the deterministic sampling schemes, the data-sites are located on a regular grid but may have aninfill component. On the other hand, under the stochastic sampling schemes, locations of the data-sites are given by the realizations of a collection of independent random vectors and thus, are irregularly spaced. It is shown that scaling constants of different orders are needed for asymptotic normality under different spatial sampling schemes considered here. Further, in the stochastic case, the asymptotic covariance matrix is shown to depend on the spatial sampling density associated with the stochastic design. Results are established for M-estimators corresponding to certain non-smooth score functions including Huber’s ψ-function and the sign functions (corresponding to the sample quantiles). Research of Lahiri is partially supported by NSF grant no. DMS-0072571. Research of Mukherjee is partially supported by the Academic Research Grant R-155-000-003-112 from the National University of Singapore.     

Using stochastic optimization to determine threshold values for the control of unreliable manufacturing systems

A manufacturing system with two tandem machines producing one part type is considered in this work. The machines are unreliable, each having two states, up and down. Both surplus controls and Kanban systems are considered. Algorithms for approximating the optimal threshold values are developed. First, perturbation analysis techniques are employed to obtain consistent gradient estimates based on a single simulation run. Then, iterative algorithms of the stochastic optimization type are constructed. It is shown that the algorithms converge to the optimal threshold values in an appropriate sense. Numerical examples are provided to demonstrate the performance of the algorithms.The research of these authors was supported in part by grants from URIF, MRCO, National Science Foundation, and Wayne State University. The authors would like to thank Dr. X. R. Cao, Digital Equipment Corporation, for the valuable initial discussion and Dr. X. Y. Zhou, University of Toronto, for his helpful comments.     

Approximating infinite horizon stochastic optimal control in discrete time with constraints

Traditional approaches to solving stochastic optimal control problems involve dynamic programming, and solving certain optimality equations. When recast as stochastic programming problems, structural aspects such as convexity are retained, and numerical solution procedures based on decomposition and duality may be exploited. This paper explores a class of stationary, infinite-horizon stochastic optimization problems with discounted cost criterion. Constraints on both states and controls are permitted, and modeled in the objective function by allowing it to take infinite values. Approximating techniques are developed using variational analysis, and intuitive lower bounds are obtained via averaging the future. These bounds could be used in a finite-time horizon stochastic programming setting to find solutions numerically. Research supported in part by a grant of the National Science Foundation. AMS Classification 46N10, 49N15, 65K10, 90C15, 90C46     

Parallelizable Preprocessing Method for Multistage Stochastic Programming Problems

Stochastic programming has extensive applications in practical problems such as production planning and portfolio selection. Typically, the model has very large size and some techniques are often used to exploit the special structure of the programs. It has been noticed that the coefficient matrix may not be of full rank in the well-known scenario formulation of stochastic programming; thus, the preprocessing is often necessary in developing rapid decomposition methods. In this paper, we propose a parallelizable preprocessing method, which exploits effectively the structure of the formulation. Although the underlying idea is simple, the method turns out to be very useful in practice, since it may help us to select the nonanticipativity constraints efficiently. Some numerical results are reported confirming the usefulness of the method.This work was partially supported by the Informatics Research Center for Development of Knowledge Society Infrastructure, Graduate School of Informatics, Kyoto University, Kyoto, Japan. The work of the first author was also supported in part by the National Science Foundation of China, Grant 10571039. The work of the second author was also supported in part by the Scientific Research Grant-in-Aid from the Japan Society for the Promotion of Science. The authors are grateful to the referees for careful reading of the paper and helpful comments.This author’s work was done while he was visiting Kyoto University.     

Solving multistage stochastic network programs on massively parallel computers

Multi-stage stochastic programs are typically extremely large, and can be prohibitively expensive to solve on the computer. In this paper we develop an algorithm for multistage programs that integrates the primal-dual row-action framework with proximal minimization. The algorithm exploits the structure of stochastic programs with network recourse, using a suitable problem formulation based on split variables, to decompose the solution into a large number of simple operations. It is therefore possible to use massively parallel computers to solve large instances of these problems. The algorithm is implemented on a Connection Machine CM-2 with up to 32K processors. We solve stochastic programs from an application from the insurance industry, as well as random problems, with up to 9 stages, and with up to 16392 scenarios, where the deterministic equivalent programs have a half million constraints and 1.3 million variables. Research partially supported by NSF grants CCR-9104042 and SES-91-00216, and AFOSR grant 91-0168. Computing resources were made available by AHPCRC at the University of Minnesota, and by NPAC at Syracuse University, New York.     

Models and model value in stochastic programming

Finding optimal decisions often involves the consideration of certain random or unknown parameters. A standard approach is to replace the random parameters by the expectations and to solve a deterministic mathematical program. A second approach is to consider possible future scenarios and the decision that would be best under each of these scenarios. The question then becomes how to choose among these alternatives. Both approaches may produce solutions that are far from optimal in the stochastic programming model that explicitly includes the random parameters. In this paper, we illustrate this advantage of a stochastic program model through two examples that are representative of the range of problems considered in stochastic programming. The paper focuses on the relative value of the stochastic program solution over a deterministic problem solution.The author's work was supported in part by the National Science Foundation under Grant DDM-9215921.     

Bilinear programming and structured stochastic games

One-step algorithms are presented for two classes of structured stochastic games, namely, those with additive rewards and transitions and those which have switching controllers. Solutions to such classes of games under the average reward criterion can be derived from optimal solutions to appropriate bilinear programs. The validity of using bilinear programming as a solution method follows from two preliminary theorems, the first of which is a complete classification of undiscounted stochastic games with optimal stationary strategies. The second of these preliminary theorems relaxes the conditions of the classification theorem for certain classes of stochastic games and provides the basis for the bilinear programming results. Analogous results hold for the discounted stochastic games with the above special structures.This research was supported in part by the Air Force Office of Scientific Research and by the National Science Foundation under Grant No. ECS-850-3440.