线性和仿射闭lambda项的定量方面
Quantitative aspects of linear and affine closed lambda terms
摘要：
仿射$λ$ -项是其中每个绑定变量最多出现一次的$λ$ -项，线性$λ$ -项是其中每个绑定变量出现一次的$λ$ -项。而且只有一次在本文中，我们计算了大小为$n$的闭仿射$λ$ -项、大小为$n$的闭线性$λ$ -项、大小为$n$的仿射$β$ -范式和大小为$n$的线性$β$ -范式，给出了度量$λ$ -项大小的不同方法.从这些公式中，我们展示了我们如何可以得到的程序生成的所有条款的大小$n$为每个类。为此，我们使用了一个特定的数据结构，它是考虑到抽象层次上的所有漏洞的上下文。
英文摘要：
Affine $λ$-terms are $λ$-terms in which each bound variable occurs at most once and linear $λ$-terms are $λ$-terms in which each bound variables occurs once. and only once. In this paper we count the number of closed affine $λ$-terms of size $n$, closed linear $λ$-terms of size $n$, affine $β$-normal forms of size $n$ and linear $β$-normal forms of ise $n$, for different ways of measuring the size of $λ$-terms. From these formulas, we show how we can derive programs for generating all the terms of size $n$ for each class. For this we use a specific data structure, which are contexts taking into account all the holes at levels of abstractions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
