加权上下文无关语言的无冗余随机生成
Non-redundant random generation from weighted context-free languages
摘要：
我们解决的非冗余随机生成的k个字的长度为n从上下文无关的语言。此外，我们希望避免预定义的单词集。我们研究了拒绝为基础的方法，其时间复杂度的限制，在某些情况下，在k呈指数增长。我们提出了一种替代的递归算法，其仔细的实施允许非冗余生成的k个字的大小为n的O（kn log n）的算术运算后，预先计算的O（n）的数字。因此，总体复杂度由k个字的生成主导，并且非冗余的成本可以忽略不计。
英文摘要：
We address the non-redundant random generation of k words of length n from a context-free language. Additionally, we want to avoid a predefined set of words. We study the limits of a rejection-based approach, whose time complexity is shown to grow exponentially in k in some cases. We propose an alternative recursive algorithm, whose careful implementation allows for a non-redundant generation of k words of size n in O(kn log n) arithmetic operations after the precomputation of O(n) numbers. The overall complexity is therefore dominated by the generation of k words, and the non-redundancy comes at a negligible cost.
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。
