Distribution-free stochastic optimization: advances in solution methods and applications

无分布随机优化：求解方法和应用的进展

• 批准号: 311814-2010
• 负责人: Zuluaga, Luis
• 金额: $ 1.46万
• 依托单位: University of New Brunswick
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2010
• 资助国家: 加拿大
• 起止时间: 2010-01-01 至 2011-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=457151
• 关键词: Distribution; free; stochastic; optimization; advances

Many real-world optimization problems involve random inputs for which it is difficult to make exact distributional assumptions; among others, due to the scarcity of data, and/or the very high volatility of the available data. Distribution-free stochastic optimization refers to decision making problems where only partial knowledge of the random inputs' distribution is assumed. Distribution-free (stochastic) optimization techniques have been successfully applied to decision making problems in game theory, inventory/supply chain management, combinatorial optimization, portfolio allocation, option pricing, risk management, and actuarial science. More generally, distribution-free optimization techniques are used to make "robust" decisions; that is, decisions that "work well" in many scenarios.

许多现实世界的优化问题涉及随机输入，很难做出精确的分布假设;其中，由于数据的稀缺性和/或可用数据的非常高的波动性。无分布随机优化是指仅假设随机输入的分布的部分知识的决策问题。无分布（随机）优化技术已成功地应用于博弈论，库存/供应链管理，组合优化，投资组合分配，期权定价，风险管理和精算科学中的决策问题。更一般地说，无分布优化技术用于做出“鲁棒”决策;也就是说，在许多情况下“工作良好”的决策。