Non-redundant random generation algorithms for weighted context-free languages
Abstract
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 a rejection-based approach, whose worst-case time complexity is shown to grow exponentially with $k$ for some specifications and in the limit case of a coupon collector. We propose two algorithms respectively based on the recursive method and on an unranking approach. We show how careful implementations of these algorithms allows for the non-redundant generation of $k$ words of size $n$ in $\mathcal{O}(k\cdot n\cdot \log{n})$ arithmetic operations, after precomputation of $\Theta(n)$ numbers. The overall complexity is therefore dominated by the generation of $k$ words, and the non-redundancy comes at a negligible cost.
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