Global convergence of a primal-dual interior-point method for nonlinear programming
Global convergence of a primal-dual interior-point method for nonlinear programming
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发表时间：
2008
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通讯作者：
Igor Griva;D. Shanno;R. Vanderbei;Hande Y. Benson
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作者：
Igor Griva;D. Shanno;R. Vanderbei;Hande Y. Benson
许多最近的收敛性结果获得的原始-对偶邻点方法的非线性规划，使用的假设生成迭代的有界性。在本文中，我们取代这些假设的NLP问题的新的假设，开发一个修改的原始-对偶的邻近点的方法在软件包LOQO实现和分析收敛的新方法从任何初始猜测。
Many recent convergence results obtained for primal-dual interior-point methods for nonlinear programming, use assumptions of the boundedness of generated iterates. In this paper we replace such assumptions by new assumptions on the NLP problem, develop a modification of a primal-dual interior-point method implemented in software package LOQO and analyze convergence of the new method from any initial guess.