Sequential quadratic programming for large-scale nonlinear optimization

被引：181
|
作者：
Boggs, PT [1 ]
Tolle, JW
机构：
[1] Sandia Natl Labs, Comp Sci & Math Res Dept, Livermore, CA 94550 USA
[2] Univ N Carolina, Dept Operat Res & Math, Chapel Hill, NC 27514 USA
来源：
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS | 2000年 / 124卷 / 1-2期
关键词：
sequential quadratic programming; nonlinear optimization; Newton methods; interior-point methods; local; trust-region methods convergence; global convergence;
D O I：
10.1016/S0377-0427(00)00429-5
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
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. (C) 2000 Elsevier Science B.V. All rights reserved.
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页码：123 / 137
页数：15
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