修正积分水平集方法的随机实现算法

本文提出了一个从随机到确定性的变测度算法，通过对不同的箱子采用不同的测度，将Monte-Carlo随机投点与确定性数论方法相结合的策略，使水平值充分地下降．最后，给出了实现算法收敛性并通过数值实验验证了其有效性．     

有约束的变测度积分——水平集算法

提出了一种有约束的变测度积分-水平集的算法,对不同的箱子采用不同的测度,结合确定性数论方法选取一致分布佳点集来代替Monte-Carlo随机投点,使水平值充分地下降,更快地到达全局最小,从而提高算法的计算效率.给出了算法的收敛性证明,并通过数值算例验证了它的有效性.     

一个求总极值的变测度算法及其收敛性

利用积分中值定理阐述了积分型方法的实质,指出了其优点与不足,提出相应的改进方法—变测度算法,并对变测度算法的收敛性进行了证明.     

变测度的积分型全局优化算法

郑权等首先提出积分-水平集求总极值的方法,实现算法中采用Monte-Carlo 随机投点产生近似水平集来缩小搜索区域范围,但这一算法可能失去总极值点．此后,邬 冬华等给出了一种修正的积分－水平集的方法,一种区域不收缩的分箱方法以保证总极 值点不被丢失．本文在此基础上采取对不同的箱子采用不同的测度这一策略,使水平值 更充分的下降,更快的达到全局极小值,以提高修正算法的计算效率．最后给出的数值算 例说明了算法是有效的．     

一种运用Vague集理论转化区间运输规划的方法

为解决区间型运输问题的清晰化处理问题,将Vague集当中真假隶属度函数与必要性测度和可能性测度有机的联系起来;将区间型运输问题的约束条件分为两大类等价的问题进行研究;针对供需区间型运输规划问题的特点,利用截集的思想实现了不确定性运输问题向确定性运输问题的转化.     

广义超立方体网络的寻径算法

利用H amm ing距离概念,在广义超立方体中的点对之间提出两个基于H amm ing距离的确定性寻径算法—算法1、算法2,分析了这两种算法的特点.为了克服确定性寻径算法易出现的通道拥挤现象,文中提出了一种自适应算法—算法3.     

实分析教学杂记

以区间长度、曲边梯形及凸多边形概念为基础分别引入点集测度、下方图形与凸集概念.     

实分析教学杂记

以区间长度、曲边梯形及凸多边形概念为基础分别引入点集测度、下方图形与凸集概念.     

混料试验的D—最优对称设计,算法及应用

本文着重研究了混料试验的D—最优对称设计.基于Fedorov及Atwood的迭代方法,作者给出一个构造D—最优对称设计的改进算法.这个新算法由双循环迭代构成:从初始设计中减去最小方差对称点的设计测度;增加设计测度于最大方差的对称设计点,同时,本算法还只在对称子区域中寻找最大方差设计点,这样就使得Fedorov算法的收敛速度有了显著地提高,并能构造出更高效的D—最优对称设计.另外还给出一些构造实例.     

基于可信性测度的模糊线性不等式组的确定性等价

在模糊优化决策问题中,约束条件包含模糊线性不等式组.如何把它们转化为确定性的等价形式,这在模糊控制和模糊决策中起着重要的作用.可信性测度是模糊数学实现公理化体系的一个基础.本文在可信性测度的基础上,探讨了模糊约束在一定的满意度水平之下转化为确定性的等价式,同时就三角形模糊变量给出了模糊线性不等式组的确定性等价的具体表达式.     

Towards global bilevel dynamic optimization

The global solution of bilevel dynamic optimization problems is discussed. An overview of a deterministic algorithm for bilevel programs with nonconvex functions participating is given, followed by a summary of deterministic algorithms for the global solution of optimization problems with nonlinear ordinary differential equations embedded. Improved formulations for scenario-integrated optimization are proposed as bilevel dynamic optimization problems. Solution procedures for some of the problems are given, while for others open challenges are discussed. Illustrative examples are given.     

数学期望型水平值逼近全局最小值的方法

郑权在1978年提出的一种积分水平集算法概念性算法.由于水平集一般情况下难以求出,此算法通过Monte-Carlo随机取点来实现.本文提出了数学期望型水平值逼近全局最小值的概念性算法,它利用了相对熵主要思想,通过改变重要样本密度函数,克服了郑权算法水平集不易求得而难以求出水平值的困难.本文还给出了求全局最小值的收敛准则并证明了它的渐进收敛性.     

全局优化问题的一个无参数填充函数算法

填充函数法是求解全局优化问题的一种有效的确定性算法,方法的关键在于填充函数的构造.对于一般无约束优化问题提出了一个新的无参数填充函数,通过定义证明了此填充函数能保持填充性质.利用其理论性质设计了相应的算法并对几个经典的算例进行了数值实验,实验结果表明算法有效可行.     

A Rigorous Global Optimization Algorithm for Problems with Ordinary Differential Equations

The optimization of systems which are described by ordinary differential equations (ODEs) is often complicated by the presence of nonconvexities. A deterministic spatial branch and bound global optimization algorithm is presented in this paper for systems with ODEs in the constraints. Upper bounds for the global optimum are produced using the sequential approach for the solution of the dynamic optimization problem. The required convex relaxation of the algebraic functions is carried out using well-known global optimization techniques. A convex relaxation of the time dependent information is obtained using the concept of differential inequalities in order to construct bounds on the space of solutions of parameter dependent ODEs as well as on their second-order sensitivities. This information is then incorporated in the convex lower bounding NLP problem. The global optimization algorithm is illustrated by applying it to four case studies. These include parameter estimation problems and simple optimal control problems. The application of different underestimation schemes and branching strategies is discussed.     

一种修正的求约束总极值的积分-水平集方法

对于有约束的全局最优化问题,在Chew-Zheng的《Integral Global Optimization》和邬冬华等的《一种修正的求总极值的积分-水平集方法的实现算法收敛性》的基础上,给出一种修正的求约束总极值的积分-水平集方法,它同样具有修正的求总极值的积分-水平集方法的两个特点: 1) 每一步构造一个新函数,它与原目标函数具有相同的总极值; 2) 避免了郑权算法在一般情况下,由于水平集不易求得而造成难以求出水平集的困难．同时给出了其实现算法,并证明了算法的收敛性．     

SPT: a stochastic tunneling algorithm for global optimization

A stochastic approach to solving unconstrained continuous-function global optimization problems is presented. It builds on the tunneling approach to deterministic optimization presented by Barhen and co-workers (Bahren and Protopopescu, in: State of the Art in Global Optimization, Kluwer, 1996; Barhen et al., Floudas and Pardalos (eds.), TRUST: a deterministic algorithm for global optimization, 1997) by combining a series of local descents with stochastic searches. The method uses a rejection-based stochastic procedure to locate new local minima descent regions and a fixed Lipschitz-like constant to reject unpromising regions in the search space, thereby increasing the efficiency of the tunneling process. The algorithm is easily implemented in low-dimensional problems and scales easily to large problems. It is less effective without further heuristics in these latter cases, however. Several improvements to the basic algorithm which make use of approximate estimates of the algorithms parameters for implementation in high-dimensional problems are also discussed. Benchmark results are presented, which show that the algorithm is competitive with the best previously reported global optimization techniques. A successful application of the approach to a large-scale seismology problem of substantial computational complexity using a low-dimensional approximation scheme is also reported.