Truncated Newton method for sparse unconstrained optimization using automatic differentiation

When solving large complex optimization problems, the user is faced with three major problems. These are (i) the cost in human time in obtaining accurate expressions for the derivatives involved; (ii) the need to store second derivative information; and (iii), of lessening importance, the time taken to solve the problem on the computer. For many problems, a significant part of the latter can be attributed to solving Newton-like equations. In the algorithm described, the equations are solved using a conjugate direction method that only needs the Hessian at the current point when it is multiplied by a trial vector. In this paper, we present a method that finds this product using automatic differentiation while only requiring vector storage. The method takes advantage of any sparsity in the Hessian matrix and computes exact derivatives. It avoids the complexity of symbolic differentiation, the inaccuracy of numerical differentiation, the labor of finding analytic derivatives, and the need for matrix store. When far from a minimum, an accurate solution to the Newton equations is not justified, so an approximate solution is obtained by using a version of Dembo and Steihaug's truncated Newton algorithm (Ref. 1).This paper was presented at the SIAM National Meeting, Boston, Massachusetts, 1986.     

Impact of Partial Separability on Large-Scale Optimization

ELSO is an environment for the solution oflarge-scale optimization problems. With ELSO the user is required to provide only code for the evaluation of a partially separable function. ELSO exploits the partialseparability structure of the function to computethe gradient efficiently using automatic differentiation.We demonstrate ELSO's efficiency by comparing thevarious options available in ELSO.Our conclusion is that the hybrid option in ELSOprovides performance comparable to the hand-coded option, while having the significantadvantage of not requiring a hand-coded gradient orthe sparsity pattern of the partially separable function.In our test problems, which have carefully coded gradients,the computing time for the hybrid AD option is within a factor of two of thehand-coded option.     

Solid phase synthesis of azapeptides using an automatic synthesizer

Peptides containing azaglycine located terminally or within the backbone have been prepared in the solid phase by means of an automatic synthesizer.     

屋顶绿化的自动灌溉系统设计

屋顶绿化对提高城市空气质量、减少“热岛效应”、改善居住环境有积极的意义，但其灌溉环节与普通园林绿化灌溉相比有更高的要求．为了增强屋顶灌溉的科学性，根据其特点提出了建立自动灌溉系统的设想，并对灌溉过程建立了混杂自动机模型，从离散事件动态系统与连续变量动态系统的角度共同描述了自动灌溉的过程，最后给出了自动灌溉系统的工程设计方案，对屋顶绿化自动化、精细化程度的提高具有一定的启示意义．     

书籍扫描图像畸变参数自动计算方法的研究

为实现书籍扫描图像的畸变自动校正,提出用多项式来描述各像素的理论灰度g(zi)与页面上对应点到扫描仪工作平面距离zi二者之间的关系。为确立该多项式,在畸变参数已知条件下扫描一幅图像,根据已知畸变参数求出zi,即可按最小二乘法原理由各像素灰度的实际值求出多项式的各个系数。实验证明,采用4阶多项式已能满足一般要求,并求出了各系数。对任意扫描图像,自动计算畸变参数的方法为:首先利用扫描图像上页边空白处各像素的灰度,对畸变参数进行估计,并求出zi的估计值;然后代入所确立的多项式,可求得g(zi);通过调整各畸变参数的估计值,直到g(zi)与gi最为接近,即得最佳畸变参数。用于图像校正实验,获得了较好的校正效果,最大误差由不校正时的41%下降到了6.9%。这使得无需用户测量并输入有关畸变参数即可进行自动校正。     

中高分辨力遥感图像中飞机目标自动识别算法研究

提出了一种中高分辨力的航空航天遥感图像中飞机目标快速自动识别的新算法。在分割和分类过程中充分利用飞机目标的先验知识,提出了一种改进区域分割方法,并应用树分类器对飞机目标进行自动识别。所提出的改进区域分割方法较好地实现了区域分割中阈值的准确自动选取,克服了复杂背景图像中小目标的全局阈值自动分割的失效问题。采用二叉树分类器,通过提取简单的目标几何特征,分层进行种类识别,提高了识别速度,降低了漏检率和虚警率。运用该方法进行了实验。结果表明,识别率达到了100%。     

宁波港船舶交通管理系统(VTS)

通过对宁波港的交通环境，现有VTS系统及港口发展的趋势研究，提出了改善宁波港通航环境，建立和完善VTS系统的有效措施。     

旧式大型工具显微镜数显化

大型工具显微镜是用途较广的精密长度计量仪器,主要用于测量螺纹中径、圆柱的直径等。目前实验室和工厂现有的仪器属50年代生产的老型号产品,用目视螺旋测微鼓上的刻度读数,人工记录和处理数据,测量时费时费工。针对大型工具显微镜数显化的问题,阐述了光栅、光电显微镜、单片机、新型传感器在数显上的应用。利用新技术和新型传感器改造老设备,使其实现数字化和智能化,以提高测量效率和测量精度。改造的结果表明,改造后的仪器测量效率大大提高,精度可提高一个数量级。     

10 Gb/s光纤通信系统一阶偏振模色散动态补偿系统反馈控制的实现

在偏振模色散(PMD)自动补偿技术中,如何根据反馈信号得到相应的控制信号,使补偿速度跟随偏振模色散变化始终是该技术的一个核心问题。提出了一种新颖的自适应抖动跟踪算法,完成了以微波信号为反馈的多自由度的一阶偏振模色散自动反馈补偿系统的跟踪补偿实验。算法成功地解决了传统算法在跟踪搜索过程中易陷入局部极值的问题,有效地克服了系统中的重要控制器件偏振控制器的磁滞现象以及动态补偿时跟踪搜索过程中易出现的瞬间恶化现象。实验结果表明该算法在对出现突发偏振模色散扰动后自动进行补偿的响应速度在ms量级,最快能达到1~2 ms。