有界约束极小化问题的多层有效集置信域（MASTR）方法
Multilevel Active-Set Trust-Region (MASTR) Method for Bound Constrained Minimization
摘要：
我们介绍了一种新的变体的递归多级置信域（RMTR）方法，称为MASTR。该方法用于求解由偏微分方程的有限元离散引起的非凸有界约束极小化问题。MASTR利用基于截断基方法的活动集策略，以保留由较粗水平在最细水平上定义的可变界限。这种方法的使用允许MASTR方法的快速收敛，特别是一旦检测到精确的活动集。数值算例表明了该方法的有效性。
英文摘要：
We introduce a novel variant of the recursive multilevel trust-region (RMTR) method, called MASTR. The method is designed for solving non-convex bound-constrained minimization problems, which arise from the finite element discretization of partial differential equations. MASTR utilizes an active-set strategy based on the truncated basis approach in order to preserve the variable bounds defined on the finest level by the coarser levels. Usage of this approach allows for fast convergence of the MASTR method, especially once the exact active-set is detected. The efficiency of the method is demonstrated by means of numerical examples.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
