马氏决策过程平均准则最优不等式综述

对平均准则的讨论一直是马氏决策过程研究的热点之一，近几年已从最优方程推广到最优不等式。本文系统地了介绍最优不等式的提出及其发展思路，目前已取得的成果等，同时也指出了有待于进一步研究的问题。     

约束折扣半马氏决策规划

本文研究约束折扣半马氏决策规划问题，即在一折扣期望费用约束下，使折扣期望报酬达最大的约束最优问题，假设状态集可数，行动集为紧的非空Ｂｏｒｅｌ集，本文给出了ｐ－约束最优策略的充要条件，证明了在适当的假设条件下必存在ｐ－约束最优策略。     

多目标决策非凸锥Λ－有效点的存在性与控制性质

本文提出了多目标决策偏好及最优解的一般概念和集诱导偏好的概念．给出了判断ρ－完备集的一系列条件，从而指出了ρ－完备集是十分广泛的集类．得到了集合的Λ－有效点的存在性定理和ρ－下闭集与截面的Λ－有效点的性质．通过引入函数Λ－下半连续的概念，得到了多目标决策一般集诱导偏好最优解的存在性定理．在这些结果的基础上，最后得到了集合Ｙ关于Λ和多目标决策问题的控制性质．     

概率区间型决策的扩充

本文将概率区间型决策进行混合扩充，并给出了对抗条件下和非对抗条件下最优解的求法。     

连续时间马尔可夫决策过程的折扣模型

本文考虑的是转移速率族任意且费用率函数可能无界的连续时间马尔可夫决策过程的折扣模型．放弃了传统的要求相应于每个策略的　Ｑ　－过程唯一等条件,而首次考虑相应每个策略的　Ｑ　－过程不一定唯一,　转移速率族也不一定保守,　费用率函数可能无界,　且允许行动空间非空任意的情形．　本文首次用"α－折扣费用最优不等式"更新了传统的α－折扣费用最优方程,并用"最优不等式"和新的方法,不仅证明了传统的主要结果即最优平稳策略的存在性,　而且还进一步探讨了（　∈＞０　　）－最优平稳策略,具有单调性质的最优平稳策略,　以及（∈≥０）　－最优决策过程的存在性,　得到了一些有意义的新结果．　最后,　提供了一个迁移率受控的生灭系统例子,　它满足本文的所有条件,　而传统的假设（见文献［１－１４］）均不成立．     

报酬函数及转移速率族均非一致有界的连续时间折扣马氏决策规划

本文首次在报酬函数及转移速率族均非一致有界的条件下，对可数状态空间，可地动集的连续时间折扣马氏决策规划进行研究，文中引入一类新的无界报酬函数，在一类新的马氏策略中，讨论了最优策略的存在性及春结构，除证明了在有界报酬和一致有界转移速率族下成立的主要结果外，本文还得到一些重要结论。     

基于模拟植物生长算法的协作研发网络决策优化模型

在协作研发网络决策中,合理的投资组合可使企业获得理想的收益。企业协作研发网络的投资组合是多方博弈后的结果,利用模拟植物生长算法构建的优化模型可以分析企业在网络中投资组合的博弈过程。通过模拟植物生长算法计算得到的全局最优解和局部最优解是企业协作研发决策投资组合的最优决策集。企业可以根据策略集调整自身的投资方式,制定最优的决策方案。     

报酬无界的连续时间折扣马氏决策规划

本文讨论了报酬函数夫界，转移速率族一致有界，状态空间和行动集均可数的连续时间折扣马氏决策规划，文中引入了一为新的无界报酬函数，并在一新的马氏策略类中，证明了有界报酬下成立的所有结果。讨论了最优策略的结构，得到了该模型策略为最优的一个充要条件。     

Fuzzy系统的多阶段决策问题

本文讨论系统的状态、决策为Fuzzy子集的多阶段决策问题。对Fuzzy约束、Fuzzy目标、Fuzzy判决函数等概念给予新的定义。并按照最优化原理,对文中所给出的三种判决函数作了深入讨论。最后,给出一个求最优策略的算法。     

一类极大极小随机规划逼近问题最优解集的上半收敛性

本文首先将极大极小随机规划等价的转化为一个二层随机规划,在下层初始随机规划最优解集为多点集的情形下,给出下层随机规划逼近问题最优解集集值映射关于上层决策变量参数的上半收敛性和最优值函数的连续性.然后将上层随机规划等价转化为以上层和下层决策变量作为整体决策变量,以下层规划最优解集的图作为约束条件的单层规划,并在下层初始随机规划最优解集的图为正则的条件下,得到上层随机规划逼近问题最优解集关于最小信息概率度量收敛的上半收敛性.     

Uniformly overtaking and weakly overtaking optimal solutions in infinite-horizon optimal control: When optimal solutions are agreeable

In this paper, we investigate the relationship between two classes of optimality which have arisen in the study of dynamic optimization problems defined on an infinite-time domain. We utilize an optimal control framework to discuss our results. In particular, we establish relationships between limiting objective functional type optimality concepts, commonly known as overtaking optimality and weakly overtaking optimality, and the finite-horizon solution concepts of decision-horizon optimality and agreeable plans. Our results show that both classes of optimality are implied by corresponding uniform limiting objective functional type optimality concepts, referred to here as uniformly overtaking optimality and uniformly weakly overtaking optimality. This observation permits us to extract sufficient conditions for optimality from known sufficient conditions for overtaking and weakly overtaking optimality by strengthening their hypotheses. These results take the form of a strengthened maximum principle. Examples are given to show that the hypotheses of these results can be realized.This research was supported by the National Science Foundation, Grant No. DMS-87-00706, and by the Southern Illinois University at Carbondale, Summer Research Fellowship Program.     

Bias Optimality versus Strong 0-Discount Optimality in Markov Control Processes with Unbounded Costs

This paper deals with expected average cost (EAC) and discount-sensitive criteria for discrete-time Markov control processes on Borel spaces, with possibly unbounded costs. Conditions are given under which (a) EAC optimality and strong –1-discount optimality are equivalent; (b) strong 0-discount optimality implies bias optimality; and, conversely, under an additional hypothesis, (c) bias optimality implies strong 0-discount optimality. Thus, in particular, as the class of bias optimal policies is nonempty, (c) gives the existence of a strong 0-discount optimal policy, whereas from (b) and (c) we get conditions for bias optimality and strong 0-discount optimality to be equivalent. A detailed example illustrates our results.     

On the No-Gap Second-Order Optimality Conditions for a Discrete Optimal Control Problem with Mixed Constraints

Motivated by our recent works on optimality conditions in discrete optimal control problems under a nonconvex cost function, in this paper, we study second-order necessary and sufficient optimality conditions for a discrete optimal control problem with a nonconvex cost function and state-control constraints. By establishing an abstract result on second-order optimality conditions for a mathematical programming problem, we derive second-order necessary and sufficient optimality conditions for a discrete optimal control problem. Using a common critical cone for both the second-order necessary and sufficient optimality conditions, we obtain “no-gap” between second-order optimality conditions.     

On strong and weak second-order necessary optimality conditions for nonlinear programming

Second-order necessary optimality conditions play an important role in optimization theory. This is explained by the fact that most numerical optimization algorithms reduce to finding stationary points satisfying first-order necessary optimality conditions. As a rule, optimization problems, especially the high dimensional ones, have a lot of stationary points so one has to use second-order necessary optimality conditions to exclude nonoptimal points. These conditions are closely related to second-order constraint qualifications, which guarantee the validity of second-order necessary optimality conditions. In this paper, strong and weak second-order necessary optimality conditions are considered and their validity proved under so-called critical regularity condition at local minimizers.     

On optimality conditions of relaxed non-convex variational problems

Non-convex variational problems in many situations lack a classical solution. Still they can be solved in a generalized sense, e.g., they can be relaxed by means of Young measures. Various sets of optimality conditions of the relaxed non-convex variational problems can be introduced. For example, the so-called “variations” of Young measures lead to a set of optimality conditions, or the Weierstrass maximum principle can be the base of another set of optimality conditions. Moreover the second order necessary and sufficient optimality conditions can be derived from the geometry of the relaxed problem. In this article the sets of optimality conditions are compared. Illustrative examples are included.     

New Results on Second-Order Optimality Conditions in Vector Optimization Problems

In this paper, we study second-order optimality conditions for multiobjective optimization problems. By means of different second-order tangent sets, various new second-order necessary optimality conditions are obtained in both scalar and vector optimization. As special cases, we obtain several results found in the literature (see reference list). We present also second-order sufficient optimality conditions so that there is only a very small gap with the necessary optimality conditions. The authors thank Professor P.L. Yu and the referees for valuable comments and helpful suggestions.     

Optimal Control of the Instationary Three Dimensional Navier-Stokes-Voigt Equations

We study an optimal control problem with quadratic objective functional for the three dimensional Navier-Stokes-Voigt equations in bounded domains. We show the existence of optimal solutions, the necessary optimality conditions and the sufficient optimality conditions. The second-order optimality conditions obtained in the article seem to be optimal.     

No-gap optimality conditions for an optimal control problem with pointwise control-state constraints

An optimal control problem with pointwise mixed constraints of the instationary three-dimensional Navier–Stokes–Voigt equations is considered. We derive second-order optimality conditions and show that there is no gap between second-order necessary optimality conditions and second-order sufficient optimality conditions. In addition, the second-order sufficient optimality conditions for the problem where the objective functional does not contain a Tikhonov regularization term are also discussed.     

Approximate optimality conditions for minimax programming problems

In this article we study non-smooth Lipschitz programming problems with set inclusion and abstract constraints. Our aim is to develop approximate optimality conditions for minimax programming problems in absence of any constraint qualification. The optimality conditions are worked out not exactly at the optimal solution but at some points in a neighbourhood of the optimal solution. For this reason, we call the conditions as approximate optimality conditions. Later we extend the results in terms of the limiting subdifferentials in presence of an appropriate constraint qualification thereby leading to the optimality conditions at the exact optimal point.     

Some kind of Pareto stationarity for multiobjective problems with equilibrium constraints

ABSTRACT

In this paper, we derive some optimality and stationarity conditions for a multiobjective problem with equilibrium constraints (MOPEC). In particular, under a generalized Guignard constraint qualification, we show that any locally Pareto optimal solution of MOPEC must satisfy the strong Pareto Kuhn-Tucker optimality conditions. We also prove that the generalized Guignard constraint qualification is the weakest constraint qualification for the strong Pareto Kuhn-Tucker optimality. Furthermore, under certain convexity or generalized convexity assumptions, we show that the strong Pareto Kuhn-Tucker optimality conditions are also sufficient for several popular locally Pareto-type optimality conditions for MOPEC.