论文标题
将回顾性近似与重要性取样相结合以优化处于风险的条件价值
Combining Retrospective Approximation with Importance Sampling for Optimising Conditional Value at Risk
论文作者
论文摘要
本文研究了回顾性近似解决方案范式在通过重要性抽样（IS）有效地解决避免风险优化问题的情况下。尽管在估算诸如风险的条件价值（CVAR）等尾巴风险措施时可以解决庞大的样本要求，但它在CVAR驱动的优化问题中的使用是复杂的，因此需要定制措施的变化与不同的优化迭代率不同，因此出现了循环。提出的算法通过采用单变量克服了这些挑战，这是在回顾性近似程序中提供均匀方差降低的转换，非常适合调整IS参数选择。由此产生的基于模拟的近似方案既享有回顾近似赋予的计算效率，又享有对数的降低，而对对数有效的差异降低。
This paper investigates the use of retrospective approximation solution paradigm in solving risk-averse optimization problems effectively via importance sampling (IS). While IS serves as a prominent means for tackling the large sample requirements in estimating tail risk measures such as Conditional Value at Risk (CVaR), its use in optimization problems driven by CVaR is complicated by the need to tailor the IS change of measure differently to different optimization iterates and the circularity which arises as a consequence. The proposed algorithm overcomes these challenges by employing a univariate IS transformation offering uniform variance reduction in a retrospective approximation procedure well-suited for tuning the IS parameter choice. The resulting simulation based approximation scheme enjoys both the computational efficiency bestowed by retrospective approximation and logarithmically efficient variance reduction offered by importance sampling
