CIESC Journal

• RESEARCH PAPERS • 上一篇    下一篇

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

仲卫涛; 邵之江; 张余岳; 钱积新   

  1. Institute of systems Engineering, State Key Lab of Industrial Control Technology, Zhejiang
    University, Hangzhou 310027, China
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2000-09-28 发布日期:2000-09-28
  • 通讯作者: 仲卫涛

Applying Analytical Derivative and Sparse MatrixTechniques to Large-Scale Process
Optimization Problems

ZHONG Weitao; SHAO Zhijiang; ZHANG Yuyue; QIAN Jixin   

  1. Institute of systems Engineering, State Key Lab of Industrial Control Technology, Zhejiang
    University, Hangzhou 310027, China
  • Received:1900-01-01 Revised:1900-01-01 Online:2000-09-28 Published:2000-09-28
  • Contact: ZHONG Weitao

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 effi ciency, which shows this
strategy is promising and suitable for large-scale process optimization problems.

关键词: large-scale optimization;open-equation;sequential quadratic programming;analytical derivative;sparse matrix technique

Abstract: 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 effi ciency, which shows this
strategy is promising and suitable for large-scale process optimization problems.

Key words: large-scale optimization, open-equation, sequential quadratic programming, analytical derivative, sparse matrix technique