一个线性约束优化问题的广义梯度投影法

本文对线性约束优化问题提出了一个新的广义梯度投影法，该算法采用了非精确线性搜索，并在每次迭代运算中结合了广义投影矩阵和变尺度方法的思想确定其搜索方向．在通常的假设条件下，证明了该算法的整体收敛性和超线性收敛速度．     

Local Feasible QP-Free Algorithms for the Constrained Minimization of SC1 Functions

We consider the problem of minimizing an SC¹ function subject to inequality constraints. We propose a local algorithm whose distinguishing features are that: (a) a fast convergence rate is achieved under reasonable assumptions that do not include strict complementarity at the solution; (b) the solution of only linear systems is required at each iteration; (c) all the points generated are feasible. After analyzing a basic Newton algorithm, we propose some variants aimed at reducing the computational costs and, in particular, we consider a quasi-Newton version of the algorithm.     

高频聚焦换能器声场的激光测量法

本文描述了一种使用激光测振技术测量高频聚焦换能器声场布的方法，并给出了使用该方法对一种高频聚换能器进行测量的结果，通过理论计算对结果的正确性进行了验证。     

数字全息图变频采样研究

根据Whittaker-Shannon抽样定理,对菲涅耳全息图进行变频率采样,保证整个全息图各部分采样频率为该处相应空间频率的两倍,从而完全消除数字全息图由于采样冗余而带来全息图的信息冗余.并且,对变频采样后的全息图进行线性插值恢复即可获得良好的再现像质.实验和理论均证明了该方法的有效性和可行性,为全息图的信息存贮与传输提出一条新的思路.     

风力发电机组变速恒频控制系统研究

全面介绍了一种最新应用于风力发电中的新型电机—无刷双馈电机，并分析了这种新型电机作变速恒频运行的原理并对这种电机进行了动态特性仿真研究，给出了形式简洁的控制方程和易于实现的控制方案。     

一种改进的进化规划算法

本对于全局优化问题提出一个改进的进化规划算法，该算法以概率p接收基于电磁理论求出合力方向作为随机搜索方向，以概率1-p接收按正态分布产生的随机搜索方向。改进算法不仅克服了传统进化规划算法随机搜索的盲目性，而且保留了传统进化规划算法全局搜索性。本算法应用于几个典型例题，数值结果表明本算法是可行的，有效的。     

多心理账户下机构投资者的风险优化研究

从决策有限理性角度，引入行为金融理论于机构投资风险优化系统，对多心理账户条件下机构投资的风险优化设计进行了研究。以Friedman和Savage之谜为释例，对机构投资风险优化中的诸多非标准金融异像进行了解释。以Shefrin和Statman行为证券组合理论为核心，建立了多心理账户条件下机构投资的风险优化模型，为机构投资的风险优化实践提供了一种量化分析工具。     

滤波器对偏振模色散的补偿

根据耦合非线性薛定谔方程 ,本文研究了偏振模色散如何影响各种码型的频谱 ,分析了滑频滤波器和固定滤波器对偏振模色散的补偿作用。研究结果表明 :偏振模色散对不同码型的频谱影响是不一样的 ,对色散管理孤子的频谱影响最大 ,产生新的频率分量也最多 ,而且每个新频率分量都较强 ,比入射频谱展宽许多 ;滑频滤波器和固定滤波器对偏振模色散都具有补偿作用 ,使最大可传输距离成倍增加 ,两种滤波器对偏振模色散的补偿作用相差较小 ;但是对色散管理孤子系统中的偏振模色散补偿效果最佳。     

线性均衡约束最优化的一个广义投影强次可行方向法

本文讨论带线性均衡约束最优化问题,首先利用摄动技术和一个互补函数将问题等价转化为一般约束最优化问题,然后结合广义投影技术和强次可行方向法思想,建立了问题的一个新算法.算法在迭代过程中保证搜索方向不为零,从而使得每次迭代只需计算一次广义投影.在适当的条件下,证明了算法的全局收敛性,并对算法进行了初步的数值试验.     

The Sample Average Approximation Method Applied to Stochastic Routing Problems: A Computational Study

The sample average approximation (SAA) method is an approach for solving stochastic optimization problems by using Monte Carlo simulation. In this technique the expected objective function of the stochastic problem is approximated by a sample average estimate derived from a random sample. The resulting sample average approximating problem is then solved by deterministic optimization techniques. The process is repeated with different samples to obtain candidate solutions along with statistical estimates of their optimality gaps.We present a detailed computational study of the application of the SAA method to solve three classes of stochastic routing problems. These stochastic problems involve an extremely large number of scenarios and first-stage integer variables. For each of the three problem classes, we use decomposition and branch-and-cut to solve the approximating problem within the SAA scheme. Our computational results indicate that the proposed method is successful in solving problems with up to 2¹⁶⁹⁴ scenarios to within an estimated 1.0% of optimality. Furthermore, a surprising observation is that the number of optimality cuts required to solve the approximating problem to optimality does not significantly increase with the size of the sample. Therefore, the observed computation times needed to find optimal solutions to the approximating problems grow only linearly with the sample size. As a result, we are able to find provably near-optimal solutions to these difficult stochastic programs using only a moderate amount of computation time.