论文标题
随机优化的风险自适应方法：调查
Risk-Adaptive Approaches to Stochastic Optimization: A Survey
论文作者
论文摘要
不确定性在工程设计，数据驱动问题和决策中普遍存在。由于对假设的固有规定和歧义性，通常通过使用风险和相关概念测量表达的保守优化模型来解决不确定性。我们调查了上四分之一世纪的风险措施的快速发展。从他们在金融工程领域的开始，我们将介绍几乎所有工程和应用数学领域的传播。牢固地植根于凸分析，风险度量为处理不确定性的一般框架提供了重要的计算和理论优势。我们描述了关键事实，列出了几种具体算法，并提供了广泛的参考列表以进行进一步阅读。该调查回忆起与公用事业理论的联系和分配强大的优化，指向新兴应用领域，例如公平机器学习，并定义了可靠性的衡量标准。
Uncertainty is prevalent in engineering design, data-driven problems, and decision making broadly. Due to inherent risk-averseness and ambiguity about assumptions, it is common to address uncertainty by formulating and solving conservative optimization models expressed using measures of risk and related concepts. We survey the rapid development of risk measures over the last quarter century. From their beginning in financial engineering, we recount the spread to nearly all areas of engineering and applied mathematics. Solidly rooted in convex analysis, risk measures furnish a general framework for handling uncertainty with significant computational and theoretical advantages. We describe the key facts, list several concrete algorithms, and provide an extensive list of references for further reading. The survey recalls connections with utility theory and distributionally robust optimization, points to emerging applications areas such as fair machine learning, and defines measures of reliability.
