AI 中文总结
研究在不损失信息下提高均衡系统求解效率的变换，利用序理论结构，可将高维系统转化为低维系统，还能用于其他目的，通过经济金融应用阐述，如在实物期权问题中有显著速度提升。
AI 中文摘要
本文研究了在不损失信息的情况下提高求解均衡系统效率的变换。我们的方法利用经济问题中常见的序理论结构，以获得高维系统可转化为低维系统且保留其解之间精确关系的条件。这些变换还可用于降维以外的目的，如简化分析和促进随机近似程序。通过经济和金融应用阐述了理论观点，在一个实物期权问题中，展示了高达70000倍的速度提升。
英文摘要
This paper studies transformations that increase efficiency in solving equilibrium systems without information loss. Our approach exploits order-theoretic structure commonly found in economic problems to obtain conditions under which high-dimensional systems can be transformed into low-dimensional systems while preserving exact relationships between their solutions. The transformations can also be used for purposes other than dimensionality reduction, such as simplifying analysis and facilitating stochastic approximation routines. The theoretical ideas are illustrated using applications from economics and finance. In a real option problem, we demonstrate speed gains of up to 70,000 times.
