变差积分型约束总极值问题的不连续罚途径

结合积分途径运用不连续精确罚函数来求解全局约束最小化问题,进一步,提出了约束变差积分的一般形式并证明了其分析性质,同时也给出并证明了其全局最优性条件,并由此设计了一个新算法.基于Monte-Carlo模拟技术,运用交叉熵方法和重要样本实现了该算法,数值实验也说明了这个新算法是有效的.     

不连续系统的有界变差解

利用比Lebesgue积分更广泛的Henstock积分及其不等式，建立了不连续系统有界变差解的存在性及唯一性定理．     

对数三角函数的定积分

定义了三种积分表示的两元函数.这些两元函数有伽马函数表示,可以展开为幂级数.在积分符号内展开被积函数,先积分,再求和,也得到级数展开.对比展开系数,就得到一些对数三角函数定积分的值.选取合适的围道,得到其他两类对数三角函数定积分的值.     

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．     

对数正态分布和Weibull分布变差系数的抽样验证方案

本文在参数的极大似然估计的基础上，给出了对数正态分布和Ｗｅｉｂｕｌ分布变差系数的抽样验证方案．     

全局优化问题的一个单参数填充函数方法

利用改进的填充函数的定义,对一般的无约束最优化问题给出了一个新的单参数填充函数,分析并证明了此填充函数的性质.利用该填充函数,构造了新的算法,对此算法进行了数值实验,并将此算法做了比较,结果表明此填充函数算法是可行的.     

反常积分敛散性的对数判别法

给出一个判别无穷限反常积分敛散性的对数判别法,并通过实例说明其应用．     

全局优化问题的一类积分型最优性条件 (英)

本文通过构造水平集辅助函数对一类积分全局最优性条件进行研究. 所构造的辅助函数仅含有一个参数变量与一个控制变量,该参数变量用以表征对原问题目标函数最优值的估计,而控制变量用以控制积分型全局最优性条件的精度. 对参数变量做极限运算即可得到积分型全局最优性条件.继而给出了用该辅助函数所刻画的全局最优性的充要条件, 从而将原全局优化问题的求解转化为寻找一个非线性方程根的问题.更进一步地,若所取测度为勒贝格测度且积分区域为自然数集合的一个有限子集, 则该积分最优性条件便化为有限极大极小问题中利用凝聚函数对极大值函数进行逼近的近似系统.从而积分型全局最优性条件可以看作是该近似系统从离散到连续的一种推广.     

基于随机搜索的一个约束优化问题的全局收敛算法

讨论了具有一般约束的全局优化问题,给出该问题的一个随机搜索算法,证明了该算法依概率1收敛到问题的全局最优解．数值结果显示该方法是有效的．     

单边奇异积分交换子的加权变差不等式

本文主要研究了单边Calder\''{o}n-Zygmund奇异积分交换子的加权变差不等式,即建立了单边Calder\''{o}n-Zygmund奇异积分与Lipschitz函数生成的交换子的加权变差不等式, 同时,利用权的外推法得到了该交换子在Triebel-Lizorkin空间上的加权变差不等式.     

Optimality condition and algorithm with deviation integral for global optimization

To study the integral global minimization, a general form of deviation integral is introduced and its properties are examined in this work. In terms of the deviation integral, optimality condition and algorithms are given. Algorithms are implemented by a properly designed Monte Carlo simulation. Numerical tests are given to show the effectiveness of the method.     

A modified integral global optimization method and its asymptotic convergence

In this paper, we propose a new integral global optimization algorithm for finding the solution of continuous minimization problem, and prove the asymptotic convergence of this algorithm. In our modified method we use variable measure integral, importance sampling and main idea of the cross-entropy method to ensure its convergence and efficiency. Numerical results show that the new method is very efficient in some challenging continuous global optimization problems.     

可达到和可逼近总极小点的存在性和最优性

针对积分总极值,讨论并拓展了丰满集和丰满函数的概念,研究了拟上丰满和伪上丰满函数的总极值问题. 在总极值的变差积分最优性条件下,证明了拟上丰满函数的可达到极小点和伪上丰满函数的可逼近极小点的存在性.     

A global optimization approach for solving the convex multiplicative programming problem

We consider a convex multiplicative programming problem of the form% MathType!MTEF!2!1!+-% feaafiart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9% vqaqpepm0xbba9pwe9Q8fs0-yqaqpepae9qq-f0-yqaqVeLsFr0-vr% 0-vr0db8meaabaqaciGacaGaaeqabaWaaeaaeaaakeaacaGG7bGaam% OzamaaBaaaleaacaaIXaaabeaakiaacIcacaWG4bGaaiykaiabgwSi% xlaadAgadaWgaaWcbaGaaGOmaaqabaGccaGGOaGaamiEaiaacMcaca% GG6aGaamiEaiabgIGiolaadIfacaGG9baaaa!4A08!\[\{ f_1 (x) \cdot f_2 (x):x \in X\} \]where X is a compact convex set of ⁿ and f ₁, f ₂ are convex functions which have nonnegative values over X.Using two additional variables we transform this problem into a problem with a special structure in which the objective function depends only on two of the (n+2) variables. Following a decomposition concept in global optimization we then reduce this problem to a master problem of minimizing a quasi-concave function over a convex set in ² ₂. This master problem can be solved by an outer approximation method which requires performing a sequence of simplex tableau pivoting operations. The proposed algorithm is finite when the functions f _i, (i=1, 2) are affine-linear and X is a polytope and it is convergent for the general convex case.Partly supported by the Deutsche Forschungsgemeinschaft Project CONMIN.     

Integral global optimization method for solution of nonlinear complementarity problems

The mapping in a nonlinear complementarity problem may be discontinuous. The integral global optimization algorithm is proposed to solve a nonlinear complementarity problem with a robust piecewise continuous mapping. Numerical examples are given to illustrate the effectiveness of the algorithm.     

Logarithmic asymptotics of solutions of the large deviation problem for the boltzmann equation with small transfer of momentum

For the Boltzmann equation with small transfer of momentum, we derive a system of nonlinear integral-differential equations describing the logarithmic asymptotics of the solution to the Cauchy problem in the domain at the distanceO(1) from the support of the initial condition.Translated fromMatematicheskie Zametki, Vol. 64, No. 1, pp. 73–94, July, 1998.This research was partially supported by INTAS-RFBR under grant No. 95-91 in the case of the first author, and by INTAS under grant No. 96-0698 in the case of the second author.     

A novel metaheuristics approach for continuous global optimization

This paper proposes a novel metaheuristics approach to find the global optimum of continuous global optimization problems with box constraints. This approach combines the characteristics of modern metaheuristics such as scatter search (SS), genetic algorithms (GAs), and tabu search (TS) and named as hybrid scatter genetic tabu (HSGT) search. The development of the HSGT search, parameter settings, experimentation, and efficiency of the HSGT search are discussed. The HSGT has been tested against a simulated annealing algorithm, a GA under the name GENOCOP, and a modified version of a hybrid scatter genetic (HSG) search by using 19 well known test functions. Applications to Neural Network training are also examined. From the computational results, the HSGT search proved to be quite effective in identifying the global optimum solution which makes the HSGT search a promising approach to solve the general nonlinear optimization problem.     

A deterministic algorithm for global optimization

We present an algorithm for finding the global maximum of a multimodal, multivariate function for which derivatives are available. The algorithm assumes a bound on the second derivatives of the function and uses this to construct an upper envelope. Successive function evaluations lower this envelope until the value of the global maximum is known to the required degree of accuracy. The algorithm has been implemented in RATFOR and execution times for standard test functions are presented at the end of the paper.Partially supported by NSF DMS-8718362.     

The minimal criterion for the equivalence between local and global optimal solutions in nondifferentiable optimization problem

In the paper, a necessary and sufficient criterion it provided such that any local optimal solution is also global in a not necessarily differentiable constrained optimization problem. This criterion is compared to others earlier appeared in the literature, which are sufficient but not necessary for a local optimal solution to be global. The importance of the established criterion is illustrated by suitable examples of nonconvex optimization problems presented in the paper.     

Synthetic realization approach to fuzzy global optimization via gamma algorithm

A new approach is proposed for global optimization problems with fuzzy cost functions and fuzzy box and equality constraints. It allows one to avoid complex operations with fuzzy sets and the use of various subjective indices of choice. To resolve the contradiction between economically better solutions with low possibility of realization and a little poorer solution with higher possibility of realization, the synthetic realization is defined as certain fixed -level cut for all membership functions. Consideration of such realizations guarantees a level of credibility not less than given (0, 1] for all globally optimal solutions. Then, so defined -cuts are rectified to cut off realizations with possibility less than and to retain higher possibility realizations which are assigned credibility μ = 1 for the whole interval of possible realizations. This construction results in a set-valued band of credibility not less than for a given fuzzy cost function f̃(x) which band has crisp Lipschitz continuous lower- and upper-value functions f_*(x), f*(x) such that f_*(x) ≤ f̃(x) ≤ f*(x) for all x X̃ Rⁿ. Then, the gamma algorithm is applied to obtain the interval global optimal solution f̄₀(x) = [f⁰_*(x), f*₀(x)]. To further simplify the computations, the fuzziness in the feasible set X̃ is transferred to the function value space transforming X̃ into the crisp unit cube in Rⁿ₊ common for all fuzzy optimization problems in Rⁿ with box and equality constraints.