CAREER: Favorable Optimization under Distributional Distortions: Frameworks, Algorithms, and Applications

职业：分布扭曲下的有利优化：框架、算法和应用

基本信息

批准号：
2046426
负责人：
Weijun Xie
金额：
$ 50.18万
依托单位：
Virginia Polytechnic Institute and State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-01 至 2022-11-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2046426&HistoricalAwards=false
关键词：
CAREER Favorable Optimization under Distributional

项目摘要

This Faculty Early Career Development Program (CAREER) award will support the investigation of new methods to significantly enhance data-driven decision-making under distributional distortions. Data-driven optimization is a commonly used tool in many industries to support complex decision making, but the resulting decisions are often susceptible to poor data quality. Stochastic optimization methods, for example, may be unduly influenced by outliers, while robust optimization methods may provide solutions that are overly cautious. This research project investigates a new framework for data-driven optimization, intended to specifically take into account the sensitivity of solutions to data quality, and to develop methods to improve these decisions. In addition to new undergraduate and graduate-level course modules on optimistic optimization, the educational components of this project include a summer camp module for high school girls interested in STEM, collaboration with a local science museum, and an interactive optimization-based interdiction game. This project will establish theoretical and algorithmic foundations for Distributionally Favorable Optimization (DFO) and investigate its applications to the areas of operations engineering under distributional distortions. Distributionally Favorable Optimization incorporates methods to examine distributional assumption on the input data and select the optimal decision under the most-favorable distribution. Specifically, this research will (i) establish fundamental frameworks for DFO that can substantially reduce the effects of outliers; (ii) investigate effective decomposition-based solution schemes for solving large-scale DFO models that are computationally efficient and have attractive convergent properties; (iii) explore and exploit structures such as submodularity, clustering, and covering of nonconvex DFO models, stimulating solution algorithms with theoretical performance guarantees; and (iv) develop learning-and-optimization frameworks to explore endogenous uncertainty in the DFO models. DFO can significantly reduce the effects of outliers, potentially enabling more accurate and reliable decisions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这项教师早期职业发展计划（职业）奖将支持对新方法的调查，以显着增强在分销扭曲下以数据为导向的决策。数据驱动的优化是许多行业中通常使用的工具来支持复杂的决策，但是由此产生的决策通常容易受到数据质量差的影响。例如，随机优化方法可能会受到异常值的过度影响，而强大的优化方法可能会提供过于谨慎的解决方案。该研究项目研究了一个新的数据驱动优化框架，旨在专门考虑解决方案对数据质量的敏感性，并开发改善这些决策的方法。除了对乐观优化的新本科和研究生级课程模块外，该项目的教育组成部分还包括一个夏令营模块，适用于对STEM感兴趣的高中女生，与当地科学博物馆的合作以及基于互动优化的Instrigation Instriction Game。该项目将建立用于分配有利优化（DFO）的理论和算法基础，并调查其在分配扭曲下的运营工程领域的应用。分布有利的优化结合了在输入数据上检查分布假设的方法，并在最有利的分布下选择最佳决策。具体而言，这项研究将（i）为DFO建立基本框架，从而大大降低异常值的影响；（ii）研究有效的基于分解的解决方案方案，以解决计算上有效且具有有吸引力的收敛性能的大规模DFO模型；（iii）探索和利用结构，例如非凸DFO模型的子二次，聚类和覆盖，并具有理论性能保证的刺激解决方案算法；（iv）开发学习和优化框架以探索DFO模型中的内源性不确定性。 DFO可以显着降低异常值的影响，有可能实现更准确和可靠的决定。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估评估的评估来支持的。