An infeasible interior-point arc-search algorithm for nonlinear constrained optimization
An infeasible interior-point arc-search algorithm for nonlinear constrained optimization
复制标题
DOI：
10.1007/s11075-021-01113-w
复制
发表时间：
2019-09
影响因子：
2.1
通讯作者：
M. Yamashita;E. Iida;Yaguang Yang
中科院分区：
文献类型：
--
作者：
M. Yamashita;E. Iida;Yaguang Yang
本文提出了一种求解非线性规划问题的不可行弧搜索邻域点算法。大多数基于邻域点法的算法都属于线搜索，因为它们在由近似中心路径的搜索方向确定的直线上计算下一个邻域。我们讨论所提出的算法的收敛性。我们还进行了CUTEst基准问题的数值实验，并比较所提出的弧搜索算法的性能与线搜索算法。数值结果表明，所提出的弧搜索算法达到最优解，使用更少的迭代，但比线搜索算法更长的时间。一个修改，导致一个更快的弧搜索算法进行了讨论。
In this paper, we propose an infeasible arc-search interior-point algorithm for solving nonlinear programming problems. Most algorithms based on interior-point methods are categorized as line search since they compute a next iterate on a straight line determined by a search direction which approximates the central path. The proposed arc-search interior-point algorithm uses an arc for the approximation. We discuss convergence properties of the proposed algorithm. We also conduct numerical experiments on the CUTEst benchmark problems and compare the performance of the proposed arc-search algorithm with that of a line-search algorithm. Numerical results indicate that the proposed arc-search algorithm reaches the optimal solution using fewer iterations but longer times than a line-search algorithm. A modification that leads to a faster arc-search algorithm is also discussed.