Sequential quadratic programming for large-scale nonlinear optimization
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摘要
The sequential quadratic programming (SQP) algorithm has been one of the most successful general methods for solving nonlinear constrained optimization problems. We provide an introduction to the general method and show its relationship to recent developments in interior-point approaches, emphasizing large-scale aspects.
论文关键词：Sequential quadratic programming,Nonlinear optimization,Newton methods,Interior-point methods,Local,Trust-region methods convergence,Global convergence
论文评审过程：Received 12 July 1999, Revised 17 December 1999, Available online 10 November 2000.
论文官网地址：https://doi.org/10.1016/S0377-0427(00)00429-5
