Collaborative Research: A New Paradigm for Simulation Optimization: Marriage between Expectation-Maximization and Model-Based Optimization

协作研究：仿真优化的新范式：期望最大化与基于模型的优化的结合

• 批准号: 1130761
• 负责人: Jiaqiao Hu
• 金额: $ 19.73万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1130761&HistoricalAwards=false
• 关键词: Collaborative; Research; New; Paradigm; Simulation

The research objective of this award is to create new simulation optimization algorithms that combine rigorous theoretical performance guarantees with the robust empirical behavior of a class of random search techniques called the model-based methods. The research approach is based on integrating the principle of the well-known Expectation-Maximization (EM) algorithm from the field of statistics into model-based methods. In particular, through exploiting a novel connection to the EM algorithm, this research will investigate a unifying framework to design and implement new model-based algorithms for solving a broad class of simulation optimization problems with very modest computational effort. These algorithms will be studied in terms of their properties (such as convergence and convergence rate) using a fusion of theories and tools from EM, stochastic approximation, and Quasi-Newton methods. A variety of applications from biostatistics to electric power systems will also be tested for the purposes of evaluating the practical utility of the developed techniques and algorithms. If successful, the resulting techniques will have applicability in a wide array of industry and science sectors. Through collaboration with bio-statisticians, the developed algorithms will be applied to optimal drug dose-response experimental designs, with potential benefits to health care. In addition, the intended applications to electric power systems will also promote synergy among different disciplines. The research resulting from this project will be disseminated through publications, software development, and participation at national and international conferences. This award will also be closely integrated with the education and training of students in mathematical science and engineering by incorporating new developments into the advanced courses taught by investigators at different institutions, and promoting the participation of female students in research.

该奖项的研究目标是创建新的仿真优化算法，以将严格的理论性能与称为基于模型的方法的一类随机搜索技术的强大经验行为相结合。该研究方法基于将统计信息领域的众所周知的期望最大化（EM）算法的原理整合到基于模型的方法中。特别是，通过利用与EM算法的新型联系，本研究将研究一个统一的框架，以设计和实施基于模型的新算法，以通过非常适度的计算工作来解决广泛的仿真优化问题。这些算法将使用来自EM，随机近似和准Newton方法的理论和工具的融合来研究其特性（例如收敛和收敛率）。从生物统计学到电力系统的各种应用也将进行测试，以评估开发技术和算法的实际实用性。如果成功，则最终的技术将适用于各种各样的行业和科学领域。通过与生物统计学家的合作，开发的算法将应用于最佳药物剂量反应实验设计，并具有潜在的卫生保健效益。此外，电力系统的预期应用也将促进不同学科之间的协同作用。该项目产生的研究将通过出版物，软件开发以及在国家和国际会议上的参与来传播。该奖项还将通过将新的发展纳入不同机构的调查人员教授的高级课程，并促进女学生参与研究研究，从而与数学科学和工程学的学生的教育和培训紧密融合。