RCN: DIMACS/Simons Collaboration on Bridging Continuous and Discrete Optimization

RCN：DIMACS/Simons 在桥接连续和离散优化方面的合作

• 批准号: 1740425
• 负责人: Tamra Carpenter
• 金额: $ 50万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-15 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1740425&HistoricalAwards=false
• 关键词: RCN; DIMACS; Simons; Collaboration; Bridging

Optimization tools and algorithms have transformed fields ranging from biology to finance, and they touch everyday lives through more efficient supply chains, better traffic management, and more secure power grids. New applications, particularly those stemming from machine learning and data science, are now challenging the field to solve larger and more complex problems on smaller devices in less time. The field is responding with innovative approaches leading to advances such as faster algorithms for maximum flow and near-real-time approximations, more efficient interior-point methods, and faster cutting-plane methods. Many of these breakthroughs bring together ideas from both continuous and discrete optimization. The DIMACS/Simons Collaboration on Bridging Continuous and Discrete Optimization aims to accelerate progress by stimulating collaboration across the many communities of optimization. The planned activities bring together computer scientists, mathematicians, operations researchers, engineers, statisticians, and algorithm developers to advance both the foundations and applications of optimization. The project begins with an intensive program at the Simons Institute during the fall semester of 2017 that launches the collaboration and builds momentum for activities conducted over the ensuing two years as part of the DIMACS Special Focus on Bridging Continuous and Discrete Optimization. The DIMACS special focus includes seven workshops that sustain the project through the end of 2019 and expand it to include more people and more topics. The project aims to improve the performance of optimization methods in challenging real settings with the potential to positively impact society by improving traditional applications in logistics, supply chains, engineering, infrastructure, and finance, as well as growing applications in machine learning and data science. The project will involve a large number of people in various scientific communities and expose them to new ideas, new problems, and new opportunities for collaboration. Participants will be diverse across a variety of dimensions, including women and other under-represented groups; a mix of junior and senior participants; people from multiple disciplines; and both industry and academic participants. There will also be international coordination with the Centre de Recherches Mathématiques and Polytechnique Montreal.

优化工具和算法已经改变了从生物到金融的各个领域，它们通过更高效的供应链、更好的交通管理和更安全的电网触及日常生活。新的应用，特别是那些源于机器学习和数据科学的应用，现在正向该领域提出挑战，要求在更短的时间内在更小的设备上解决更大、更复杂的问题。该领域正在以创新的方法做出回应，导致了一些进步，例如更快的最大流量和近实时近似算法、更高效的内点法和更快的割平面法。这些突破中的许多都汇集了连续和离散优化的想法。DIMACS/Simons在连接连续优化和离散优化方面的合作旨在通过促进多个优化社区之间的协作来加快进度。计划中的活动将计算机科学家、数学家、运筹学家、工程师、统计学家和算法开发人员聚集在一起，以推进优化的基础和应用。该项目首先在2017年秋季学期在西蒙斯研究所开展了一项强化计划，启动了协作，并为随后两年开展的活动建立了动力，这是DIMACS特别关注衔接连续和离散优化的一部分。DIMACS特别关注包括七个研讨会，这些研讨会将项目持续到2019年底，并将其扩大到包括更多人和更多主题。该项目旨在通过改进物流、供应链、工程、基础设施和金融方面的传统应用，以及机器学习和数据科学中不断增长的应用，来提高优化方法在具有挑战性的真实环境中的性能，并有可能对社会产生积极影响。该项目将涉及不同科学界的大量人员，并使他们接触到新的想法、新的问题和新的合作机会。参与者将在不同的层面上多样化，包括妇女和其他代表性不足的群体；初级和高级参与者的混合；来自多个学科的人；以及业界和学术界的参与者。还将与蒙特利尔数学和理工学院研究中心进行国际协调。