求解线性规划问题的一种全搜索方法

在[1]基础上提出一个全搜索方法,它通常只需迭代一、二次,即可得到一个基可行解,之后依据最优性条件进行的寻优迭代,也较[2]的搜索方位更多,从而总体上效果更好。     

一个修正的SQP方法-滤子方法

序列二次规划方法（SQP）是解决非线性规划问题最有效的算法之一，但是当QP子问题不可行时算法可能会失败．而且线搜索中的罚参数的选择通常比较困难．在文献[1]中，SQP方法得到了修正，使得QP子问题可行．在本文中，我们利用滤子技术避免了罚函数的使用同时提出了带线搜索的滤子方法，最终保证了SQP方法总是可行的，而且得到了方法的全局收敛性．     

Discrete time,finite state space mean field games

In this paper we study a mean field model for discrete time, finite number of states, dynamic games. These models arise in situations that involve a very large number of agents moving from state to state according to certain optimality criteria. The mean field approach for optimal control and differential games was introduced by Lasry and Lions (2006, 2007) [3], [4], [5]. The discrete time, finite state space setting is motivated both by its independent interest as well as by numerical analysis questions which appear in the discretization of the problems introduced by Lasry and Lions. The main contribution of this paper is the exponential convergence to equilibrium of the initial-terminal value problem.     

Optimal uniform approximation on angles by entire functions

The paper discusses the best or optimal uniform approximation problem by entire functions on a closed angle Δ. This problem has been studied by M.V. Keldysch in [4], under the assumption that the functions ? subject to approximation are holomorphic in a larger angle containing Δ and there is no restriction on the growth of ? at infinity. In [8], the problem was investigated for a wider class of functions ? continuously complex differentiable on Δ, with sharper estimates on the growth of approximating entire functions, linked with the growth of ? on Δ and the differential properties of ? on the boundary of Δ. In this paper, we improve some of the results on entire approximation on angles, using new approximation ideas partially presented in [9] and [10].     

群体多目标决策联合有效解类的不变凸充分条件

对于群体多目标决策问题，文[1]引进它的联合有效解类的概念，并给出这类解的最优性必要条件，在对于问题的目标函数和约束函数附加凸性的条件下，文[2]又给出了联合有效解类的最优性充分条件，本文进一步在目标函数和约束函数具不变凸和不变广义 凸的情况下，分别给出了联合有效解类的若干最优性充分条件。     

Discounted Bayesian search problems with unknown detection probabilities

In this paper we consider three discrete-time discounted Bayesian search problems with an unknown number of objects and uncertainty about the distribution of the objects among the boxes. Moreover, we admit uncertainty about the detection probabilities. The goal is to determine a policy which finds (dependent on the search problem) at least one object or all objects with minimal expected total cost. We give sufficient conditions for the optimality of the greedy policy which has been introduced in Liebig/Rieder (1996). For some examples in which the greedy policy is not optimal we derive a bound for the error.     

Summation of almost convergent divergent sequences,topological methods

In this paper we give a characterization of the conservative matrices that sum only those divergent sequences which are not almost convergent. Such conditions are similar to those which are necessary and sufficient for a conservative matrix to sum no bounded divergent sequence (Agnew [1], Mazur-Orlicz [4], Wilansky-Zeller [6], and Zeller [7]). There are, however, some differences showing, that the results mentioned above cannot be transmitted without any restriction. Parts of this paper have been already developed by the author more detailed in [5].     

On the approximate synthesis of the optimal control of stochastic quasilinear systems with aftereffect : PMM vol. 42, no. 6, 1978, pp. 978–988

Control problems for quasilinear deterministic systems without time lag were analyzed in [1, 2]. In the present paper the control of quasilinear stochastic systems, whose theory has been presented in [3–6], is studied. The approximate synthesis of the control of stochastic systems with aftereffect is of importance since the construction of their exact optimal control is successful only in exceptional cases [7, 8]. In the paper an approximate optimal control synthesis algorithm is proposed and a method for obtaining error bounds, different from ones previously obtained [9, 10], is developed.     

非单调滤子曲率线搜索算法解无约束非凸优化

宇和濮在文[Yu Z S,Pu D G.A new nonmonotone line search technique for unconstrained optimization[J].J Comput Appl Math,2008,219:134-144]中提出了一种非单调的线搜索算法解无约束优化问题.和他们的工作不同,当优化问题非凸时,本文给出了一种非单调滤子曲率线搜索算法.通过使用海森矩阵的负曲率信息,算法产生的迭代序列被证明收敛于一个满足二阶充分性条件的点.在不需要假设极限点存在的情况下,证明了算法具有整体收敛性,而且分析了该算法的收敛速率.数值试验表明算法的有效性.     

Computational experience using an edge search algorithm for linear reverse convex programs

This paper presents computational experience with a rather straight forward implementation of an edge search algorithm for obtaining the globally optimal solution for linear programs with an additional reverse convex constraint. The paper's purpose is to provide a collection of problems, with known optimal solutions, and performance information for an edge search implementation so that researchers may have some benchmarks with which to compare new methods for reverse convex programs or concave minimization problems. There appears to be nothing in the literature that provides computational experience with a basic edge search procedure. The edge search implementation uses a depth first strategy. As such, this paper's implementation of the edge search algorithm is a modification of Hillestad's algorithm [11]. A variety of test problems is generated by using a modification of the method of Sung and Rosen [20], as well as a new method that is presented in this paper. Test problems presented may be obtained at ftp://newton.ee.ucla.edu/nonconvex/pub/.     

Success Guarantee of Dual Search in Integer Programming: p-th Power Lagrangian Method

Although the Lagrangian method is a powerful dual search approach in integer programming, it often fails to identify an optimal solution of the primal problem. The p-th power Lagrangian method developed in this paper offers a success guarantee for the dual search in generating an optimal solution of the primal integer programming problem in an equivalent setting via two key transformations. One other prominent feature of the p-th power Lagrangian method is that the dual search only involves a one-dimensional search within [0,1]. Some potential applications of the method as well as the issue of its implementation are discussed.     

Problem space local search for number partitioning

We show how simple and effective metaheuristics can be developed for the number partitioning problem using the problem space approach [1]. In a previous application of local search to number partitioning [2], it was found that the performance of simulated annealing used in conjunction with swap neighborhoods was disappointing relative to the differencing heuristic of Karmarkar and Karp [3]. Using problem space neighborhoods as an alternative to swapping, we empirically demonstrate several orders of magnitude improvement over the differencing algorithm, albeit with greater computation time. This improvement in performance comes as little surprise since a modified version of the differencing heuristic is explicitly embedded in the problem space algorithm.     

Optimization of control systems with a piecewise-linear output

A linear terminal problem of optimal control with a piecewise-linear terminal constraints is considered. On the base of the concept of a support the optimality criterion is proved and sufficient optimality condition in the form of the maximum principle is formulated. The support enables us to choose from the set of the Lagrange vectors a special one which in the terminology of linear programming is called the basic vector [1]. In the case of nondegeneracy of the support control the sufficient condition under question is proved to be necessary condition     

Partitioned Random Search for Global Optimization with Sampling Cost and Discounting Factor

The method of partitioned random search has been proposed in recent years to obtain an as good as possible solution for the global optimization problem (1). A practical algorithm has been developed and applied to real-life problems. However, the design of this algorithm was based mainly on intuition. The theoretical foundation of the method is an important issue in the development of efficient algorithms for such problems. In this paper, we generalize previous theoretical results and propose a sequential sampling policy for the partitioned random search for global optimization with sampling cost and discounting factor. A proof of the optimality of the proposed sequential sampling policy is given by using the theory of optimal stopping.     

Two-level value function approach to non-smooth optimistic and pessimistic bilevel programs

ABSTRACT

The authors' paper in Dempe et al. [Necessary optimality conditions in pessimistic bilevel programming. Optimization. 2014;63:505–533], was the first one to provide detailed optimality conditions for pessimistic bilevel optimization. The results there were based on the concept of the two-level optimal value function introduced and analysed in Dempe et al. [Sensitivity analysis for two-level value functions with applications to bilevel programming. SIAM J. Optim. 22 (2012), 1309–1343], for the case of optimistic bilevel programs. One of the basic assumptions in both of these papers is that the functions involved in the problems are at least continuously differentiable. Motivated by the fact that many real-world applications of optimization involve functions that are non-differentiable at some points of their domain, the main goal of the current paper is to extend the two-level value function approach by deriving new necessary optimality conditions for both optimistic and pessimistic versions in bilevel programming with non-smooth data.     

On the optimality of a class of designs with three concurrences

In the present paper we consider a class of unequally replicated designs having concurrence range 2 and spectrum of the form μ₁(μ₂)^v−3μ₃. Now, Jacroux’s [Some sufficient conditions for the type I optimality of block designs, J. Statist. Plann. Inference 11 (1985) 385-396] Proposition 2.4 says that a design with spectrum of the above form, if satisfies some further conditions, is type 1 optimal. Unfortunately, this proposition does not apply to our designs since they have a poor status regarding E-optimality. Yet we are able to prove the A-optimality (in the general class) of these designs using majorisation technique. A method of construction of an infinite series of our A-optimal designs has also been given.The first and only known infinite series of examples of designs satisfying Jacroux’s conditions appears to be the first one in Section 4.1 of Morgan and Srivastav [On the Type-1 optimality of nearly balanced incomplete block designs with small concurrence range, Statist. Sinica 10 (2000) 1091-1116] - hitherto referred to as [MS]. In this paper, we use majorisation technique to prove stronger optimality properties of the above mentioned designs of [MS] as well as to present simpler proof of another optimality result in [MS].     

A two-phase method for selecting IMRT treatment beam angles: Branch-and-Prune and local neighborhood search

This paper presents a new two-phase solution approach to the beam angle and fluence map optimization problem in Intensity Modulated Radiation Therapy (IMRT) planning. We introduce Branch-and-Prune (B&P) to generate a robust feasible solution in the first phase. A local neighborhood search algorithm is developed to find a local optimal solution from the Phase I starting point in the second phase. The goal of the first phase is to generate a clinically acceptable feasible solution in a fast manner based on a Branch-and-Bound tree. In this approach, a substantially reduced search tree is iteratively constructed. In each iteration, a merit score based branching rule is used to select a pool of promising child nodes. Then pruning rules are applied to select one child node as the branching node for the next iteration. The algorithm terminates when we obtain a desired number of angles in the current node. Although Phase I generates quality feasible solutions, it does not guarantee optimality. Therefore, the second phase is designed to converge Phase I starting solutions to local optimality. Our methods are tested on two sets of real patient data. Results show that not only can B&P alone generate clinically acceptable solutions, but the two-phase method consistently generates local optimal solutions, some of which are shown to be globally optimal.     

On necessary optimality conditions for systems Governed by a two point boundary value problem. I

The paper concerns a necessary optimality condition in form of a Pontryagin Minimum Principle for a system governed by a linear two point boundary value problem with homogeneous Dibichlet conditions, whereby the control vector occurs in all coefficients of the differential equation. Without any convexity assumption the optimality condition is derived using a needle-like variation of the optimal control. In case of convex local control constraints the optimality condition implies the linearized minimum principle, which we have proved in [2]. An example shows that for this linearized optimality condition the convexity of the set of all admissible controls is essential.     

On a multiplication of semigroup varieties

The role of multiplication of group varieties is well known [2], as well as the role of wreath products in such a construction [1]. In the present paper the idea of exploiting wreath products is used for defining a multiplication for semigroup varieties which differs from the operations given in [3]. In section 5 we also consider some elementary properties of the defined multiplication. Some results of the paper were announced in [9].