非光滑多目标规划的不动点算法(英)

本文讨论不动点算法在非光滑多目标规划中的应用，得到了一些新的最优性条件以及不动点与非光滑多目标的解之间的关系，并且给出了解非光滑多目标规划的不动点算法的收敛性．     

原子分子工程的设计基础—计算机及其程序设计

阐述了计算机科学对原子分子学科研究的重要作用，针对原子与分子领域中一些典型的问题，介绍了计算方法及程序设计，并对有关课题编出了计算程序。从而表明：借助于计算机可使原子与分子研究工作规范化、工程化，计算机及其程序设计是原子分子工程的设计基础     

A Quasi-Newton Penalty Barrier Method for Convex Minimization Problems

We describe an infeasible interior point algorithm for convex minimization problems. The method uses quasi-Newton techniques for approximating the second derivatives and providing superlinear convergence. We propose a new feasibility control of the iterates by introducing shift variables and by penalizing them in the barrier problem. We prove global convergence under standard conditions on the problem data, without any assumption on the behavior of the algorithm.     

区域水资源承载力模糊线性规划模型及应用

首先按照联合国教科文组织提出的资源承载力定义,给出水资源承载力的定义,建立了水资源承载力的模糊线性规划模型,然后运用这模型对江西省的近期水资源承载力问题进行研究,数据结果表明模型的应用是成功的.     

基于几何布朗运动的投资组合模型

在证券的价格过程是几何布朗运动的前提下,建立了最优投资组合的多目标规划模型,使得投资收益最大和投资风险最小,并利用线性加权和法求得有效解.最后用实例进行分析.     

An integer linear programming approach for approximate string comparison

We introduce a problem called maximum common characters in blocks (MCCB), which arises in applications of approximate string comparison, particularly in the unification of possibly erroneous textual data coming from different sources. We show that this problem is NP-complete, but can nevertheless be solved satisfactorily using integer linear programming for instances of practical interest. Two integer linear formulations are proposed and compared in terms of their linear relaxations. We also compare the results of the approximate matching with other known measures such as the Levenshtein (edit) distance.     

复形上一类混合约束的多目标规划问题

探讨了ｎ维复形上一类具有混合约束的多目标规划问题，并运用代数拓扑方法得出这类规划问题的有效解。     

Simultaneously lifting sets of binary variables into cover inequalities for knapsack polytopes

Cover inequalities are commonly used cutting planes for the 0–1 knapsack problem. This paper describes a linear-time algorithm (assuming the knapsack is sorted) to simultaneously lift a set of variables into a cover inequality. Conditions for this process to result in valid and facet-defining inequalities are presented. In many instances, the resulting simultaneously lifted cover inequality cannot be obtained by sequentially lifting over any cover inequality. Some computational results demonstrate that simultaneously lifted cover inequalities are plentiful, easy to find and can be computationally beneficial.     

A finite branch-and-bound algorithm for nonconvex quadratic programming via semidefinite relaxations

Existing global optimization techniques for nonconvex quadratic programming (QP) branch by recursively partitioning the convex feasible set and thus generate an infinite number of branch-and-bound nodes. An open question of theoretical interest is how to develop a finite branch-and-bound algorithm for nonconvex QP. One idea, which guarantees a finite number of branching decisions, is to enforce the first-order Karush-Kuhn-Tucker (KKT) conditions through branching. In addition, such an approach naturally yields linear programming (LP) relaxations at each node. However, the LP relaxations are unbounded, a fact that precludes their use. In this paper, we propose and study semidefinite programming relaxations, which are bounded and hence suitable for use with finite KKT-branching. Computational results demonstrate the practical effectiveness of the method, with a particular highlight being that only a small number of nodes are required. This author was supported in part by NSF Grants CCR-0203426 and CCF-0545514.     

On the convergence of stochastic dual dynamic programming and related methods

We discuss the almost-sure convergence of a broad class of sampling algorithms for multistage stochastic linear programs. We provide a convergence proof based on the finiteness of the set of distinct cut coefficients. This differs from existing published proofs in that it does not require a restrictive assumption.