强压缩映射与拓扑非凸优化
Strong contraction mapping and topological non-convex optimization
摘要：
强压缩映射是值域始终是定义域的子集的自映射，它存在唯一的不动点，该不动点可以通过映射的迭代来确定。我们引入一个拓扑非凸优化方法，作为强压缩映射的应用，以实现全局极小值收敛。该方法的优点是对局部极小值和初始点位置的鲁棒性。
英文摘要：
The strong contraction mapping, a self-mapping that the range is always a subset of the domain, admits a unique fixed-point which can be pinned down by the iteration of the mapping. We introduce a topological non-convex optimization method as an application of strong contraction mapping to achieve global minimum convergence. The strength of the approach is its robustness to local minima and initial point position.
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。
