非精确简化SQP方法的全局收敛性

H. Jäger, E. Sachs
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引用次数: 33

摘要

本文研究具有相等约束的无限维优化问题。基本的底层算法是一个简化的SQP方法。给出了Hilbert空间中的全局收敛性证明,并将其推广到非精确简化SQP方法。这些方法对于离散化无限维问题和求解由此产生的大规模离散化优化问题是有用的。收敛性分析考虑了非光滑范数的Maratos效应。将非精确简化SQP方法应用于离散抛物控制问题
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Global Convergence of Inexact Reduced SQP Methods
In this paper we consider infinite dimensional optimization problems with equality constraints. The basic underlying algorithm is a reduced SQP method. We give a global convergence proof in Hilbert space and extend this analysis to inexact reduced SQP methods. These methods are useful when discretizing the infinite dimensional problems and solving the resulting large scale discretized optimization problems. The convergence analysis takes into account the Maratos effect which occurs for nonsmooth norms. The inexact reduced SQP method is applied to a discretized parabolic control problem
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