Discrete Optimization: From Applications to Relaxations

这是一个关于优化的研究提案，这个领域跨越了几个学科：纯数学和应用数学、计算机科学、运筹学和工程学。数学优化之所以蓬勃发展，在一定程度上要归功于大量的应用。除了库存管理和调度等传统物流渠道外，新的应用程序还对优化方法提出了要求，以应对非常大规模的数据集和对实时答案的需求。其他应用程序寻求将博弈论策略纳入优化器目标的新模型。例如，几乎所有的在线零售商都在其运营的各个方面(执行、运输、收入管理)利用优化工具。相应地，该领域也需要新的人员，以满足对机器学习、分析和数据科学专业知识的似乎永无止境的胃口，这些领域是离散和持续优化的强大用户。

This is  a research proposal on optimization, an area which crosses several disciplines: pure and applied mathematics, computer science, operations research and engineering. Mathematical optimization is thriving in part due to a wealth of applications. In addition to traditional logistic outlets such as inventory management and scheduling, new applications put demands on optimization methodology to cope with very large scale data sets and the desire for real-time answers. Additional applications seek new models which incorporate game-theoretic strategy into the optimizer's objective. Virtually all online retailers, for instance, are leveraging optimization tools in various aspects of their operations (fulfilment, shipping, revenue management). There is a corresponding demand for new personnel in the area to meet a seemingly insatiable appetite for expertise in machine learning, analytics and data science, areas which are strong users of discrete and continuous optimization.