New-generation optimization algorithms for engineering

候选人与不列颠哥伦比亚大学(CA)建立了新的合作关系，包括在他的共同指导下的一名博士生的讨论中，导致了预印本[1]。此外，他还被邀请访问东京工业大学(2022年12月)、比萨大学(计划2023年5月)和重庆师范大学(计划2023年8月)。他的研究趋势延续了前一年的思路，更多的关注于无线搜索的凸优化方法[2]和电网扩展规划的(凸优化)方法[4]。与过去一样，在凸域中的第一次调查是为了作为潜在的非凸扩展的基础，这是本提案的最终目标。一种镜像惯性向前反射向后分裂：凸性和Lipschitz光滑性之外的全局收敛和线搜索扩展，arxiv：2212.01504,2022年[2]P Latafat，AT，L，Stella，P Patrinos.梯度局部Lipschitz连续性下凸优化的自适应近似算法，Arxiv：2301.04431,2023年[3]S·哈代，AT，K Yamamoto，H Ergan，D Van Hertem.不同市场设计下北海混合离岸资产的最优网格布局，arxiv：2301.00931,2023年

The candidate established a new collaboration with the University of British Columbia (CA), including in the discussion a PhD student under his co-supervision that resulted in the preprint [1]. In addition, he has been invited for visiting periods at the Tokyo Institute of Technology (December 2022), the University of Pisa (scheduled for May 2023), and the Chongqing Normal University (scheduled for August 2023). His research trend continues along the lines of the previous year, with additional focus on linesearch-free methods for convex optimization [2] and (convex) optimization for power-grid expansion planning [4]. As in the past, a first inquiry in the convex realm is meant to serve as foundation for potential nonconvex extensions, ultimate target of the proposal.[1] Z Wang, AT, H Ou, and X Wang. A mirror inertial forward-reflected-backward splitting: Global convergence and linesearch extension beyond convexity and Lipschitz smoothness, arXiv:2212.01504, 2022[2] P Latafat, AT, L Stella, and P Patrinos. Adaptive proximal algorithms for convex optimization under local Lipschitz continuity of the gradient, arXiv:2301.04431, 2023[3] S Hardy, AT, K Yamamoto, H Ergun, and D Van Hertem. Optimal grid layouts for hybrid offshore assets in the North Sea under different market designs, arXiv:2301.00931, 2023