Randomized directional search for nonconvex optimization

作     者：Zhang, Yuxuan Xing, Wenxun 

作者机构：Department of Mathematical Sciences Tsinghua University Beijing100084 China 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Optimization algorithms 

摘      要：Direct search methods are a class of popular global optimization algorithms for general nonconvex programs. In this paper, we propose a randomized directional search algorithm (RDSA) for globally solving nonconvex optimization problems. The convergence of RDSA to a global optimizer is established and its computational complexity is derived. Additionally, IRDSA is proposed as an improved algorithm of RDSA. We then apply both RDSA and IRDSA to nonconvex programs with multiple local optima. Results demonstrate that RDSA and IRDSA exhibit remarkable efficacy to identify the global optimum and outperform other global optimization algorithms in terms of accuracy, efficiency, and *** Codes 90C26, 90C56, 90C60 © 2024, CC BY.