An interior point algorithm for large-scale nonlinear programming
An interior point algorithm for large-scale nonlinear programming
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DOI：
10.1137/s1052623497325107
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发表时间：
1999-01-01
影响因子：
3.1
通讯作者：
Nocedal, J
中科院分区：
文献类型：
--
作者：
Byrd, RH;Hribar, ME;Nocedal, J
描述了一种求解大型非线性规划问题的新算法的设计与实现。它遵循一种障碍方法，使用序列二次规划和信赖域来解决迭代过程中出现的子问题。开发了该算法的原始版本和原始-对偶版本，并通过一组数值测试说明了它们的性能。
The design and implementation of a new algorithm for solving large nonlinear programming problems is described. It follows a barrier approach that employs sequential quadratic programming and trust regions to solve the subproblems occurring in the iteration. Both primal and primal-dual versions of the algorithm are developed, and their performance is illustrated in a set of numerical tests.