线性约束最优化的一个共轭投影梯度法

本结合共轭梯度法及梯度投影法的思想,建立线性等式约束最优化的一个新算法,称之为共轭投影梯度法。分别对二次凸目标函数和一般目标函数分析和论证了算法的重要性质和收敛性。     

开关布局中本质不同的开关线路计算

通过对开关布局问题的研究，来解决一类象开关线路一样比较特殊，比较复杂的布局优化问题。从而寻找出这类问题的全局优化算法。本应用对称群对集合的作用，轨道，不动点等讨论了开关布局问题，给出了本质不同的开关线路个数的计算公式。     

从最优规模到更大的最优规模--理性企业规模的线性优化

企业效益是以规模为基础的，不同企业最优规模及达到的方式不同。本讨论了企业为获得最优效益，在理性、线性的最优规模下处理过剩资源，单纯的规模扩张及价格与产量关系线性时的规模扩张。指出在初步最优规模，无过剩资源时，单纯的规模扩张以资源同增长的方式最优；在价格与产量关系线性时，企业有最优扩张系数，最大的最优规模。     

一类共轭梯度法的全局收敛性结果

本文证明了在Grippo-Lucidi线搜索下当βk取βk=σ1βPRPk+σ2βnewk,其中σ1≥0,σ2≥0,σ1+σ2＞0,βnewk=gTk(gk-gk-1)/-dTk-1gk-1时一类共轭梯度法的全局收敛性,并给出了此类方法良好的数值效果.     

单调全局最优化问题的凸化外逼近算法

单调优化是指目标函数与约束函数均为单调函数的全局优化问题．本文提出一种新的凸化变换方法把单调函数化为凸函数，进而把单调优化问题化为等价的凸极大或凹极小问题，然后采用Hoffman的外逼近方法来求得问题的全局最优解．我们把这种凸化方法同Tuy的Polyblock外逼近方法作了比较，通过数值比较可以看出本文提出的凸化的方法在收敛速度上明显优于Polyblock方法．     

求广义几何规划全局最优解的线性化方法

对广义几何规划问题(GGP)提出了一个确定型全局优化算法，这类优化问题能广泛应用于工程设计和非线性系统的鲁棒稳定性分析等实际问题中，使用指数变换及对目标函数和约束函数的线性下界估计，建立了GGP的松弛线性规划(RLP)，通过对RLP可行域的细分以及一系列RLP的求解过程，从理论上证明了算法能收敛到GGP的全局最优解，对一个化学工程设计问题应用本文算法，数值实验表明本文方法是可行的。     

基于指数分布的进化策略

典型的进化策略受自然进化过程的启发而成为求解全局优化问题的重要方法。传统的ES变异算子作为一个主要的进化技术是建立在正态分布的随机变量基础上的，本文提出了基于指数分布的进化策略由于采用了新的变异算子有效地减少了产生探试解的成本，从而优于传统的进化策略。     

Computation of efficient solutions of discretely distributed stochastic optimization problems

In engineering and economics often a certain vectorx of inputs or decisions must be chosen, subject to some constraints, such that the expected costs (or loss) arising from the deviation between the outputA() x of a stochastic linear systemxA()x and a desired stochastic target vectorb() are minimal. Hence, one has the following stochastic linear optimization problem minimizeF(x)=Eu(A()x b()) s.t.xD, (1) whereu is a convex loss function on ^m , (A(), b()) is a random (m,n + 1)-matrix, E denotes the expectation operator andD is a convex subset of ⁿ . Concrete problems of this type are e.g. stochastic linear programs with recourse, error minimization and optimal design problems, acid rain abatement methods, problems in scenario analysis and non-least square regression analysis.Solving (1), the loss functionu should be exactly known. However, in practice mostly there is some uncertainty in assigning appropriate penalty costs to the deviation between the outputA ()x and the targetb(). For finding in this situation solutions hedging against uncertainty a set of so-called efficient points of (1) is defined and a numerical procedure for determining these compromise solutions is derived. Several applications are discussed.     

Pure adaptive search in global optimization

Pure adaptive seach iteratively constructs a sequence of interior points uniformly distributed within the corresponding sequence of nested improving regions of the feasible space. That is, at any iteration, the next point in the sequence is uniformly distributed over the region of feasible space containing all points that are strictly superior in value to the previous points in the sequence. The complexity of this algorithm is measured by the expected number of iterations required to achieve a given accuracy of solution. We show that for global mathematical programs satisfying the Lipschitz condition, its complexity increases at mostlinearly in the dimension of the problem.This work was supported in part by NATO grant 0119/89.     

Global optimization approach to nonlinear optimal control

To determine the optimum in nonlinear optimal control problems, it is proposed to convert the continuous problems into a form suitable for nonlinear programming (NLP). Since the resulting finite-dimensional NLP problems can present multiple local optima, a global optimization approach is developed where random starting conditions are improved by using special line searches. The efficiency, speed, and reliability of the proposed approach is examined by using two examples.Financial support from the Natural Science and Engineering Research Council under Grant A-3515 as well as an Ontario Graduate Scholarship are gratefully acknowledged. All the computations were done with the facilities of the University of Toronto Computer Centre and the Ontario Centre for Large Scale Computations.