摘要
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.
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.
基金
Supported by the National Natural Science Foundation of China(No.29906010).
关键词
解析导数
稀疏矩阵技术
大规模过程优化命题
应用
连续二次设计
化工过程
large-scale optimization, open-equation, sequential quadratic programming, analytical derivative, sparse matrix technique
