解析导数及稀疏矩阵技术在大规模过程优化命题中的应用

The performance of analytical derivative and sparse matrix techniques applied to a traditional dense sequential quadratic programming (SQP) is studied, and the strategy utilizing those techniques is also presented.Computational results on two typical chemical optimization problems demonstrate significant enhancement in efficiency, which shows this strategy is promising and suitable for large-scale process optimization problems.     

一种化工过程优化的稀疏SQP算法

根据开放式方程模型结构统一、所得优化命题普遍稀疏的特点 ,提出了一种稀疏SQP算法 .利用一阶 /二阶导数构造Hessian矩阵 ,保持了系统的稀疏结构 .通过一个预处理过程获得命题的稀疏结构信息 ,显著减少构造高维矩阵所需工作量 .计算示例表明 ,该算法优于传统SQP法 ,也表明该算法的有效性