We study the principal component analysis based approach introduced by Reisinger & Wittum (2007) and the comonotonic approach considered by Hanbali & Linders (2019) for the approximation of American basket option values via multidimensional partial differential complementarity problems (PDCPs). Both approximation approaches require the solution of just a limited number of low-dimensional PDCPs. It is demonstrated by ample numerical experiments that they define approximations that lie close to each other. Next, an efficient discretisation of the pertinent PDCPs is presented that leads to a favourable convergence behaviour.

翻译：我们研究了Reisager & Wittum(2007年)采用的主要组成部分分析法,以及Hanbali & Linders(2019年)考虑的通过多维部分差异互补问题(PDCPs)近似美国篮子选择值的共热分析法(2019年),这两种近距离分析法都要求只解决数量有限的低维多的PDCPs,通过大量数字实验可以证明它们定义了彼此相近的近似值。