根据Boltzmann分布进行随机抽选硬币
Coin selection by Random Draw according to the Boltzmann distribution
AI总结：
本文提出了一种基于Boltzmann分布的硬币选择算法，通过概率方法优化令牌选择，减少尘埃生成并限制令牌池大小，提升性能与隐私保护。
摘要：
硬币选择指的是在基于令牌的支付系统（如加密货币或中央银行数字货币（CBDC））中选择一组令牌来资助交易的问题。在本文中，我们提出了Boltzmann抽选，这是一种受统计物理原理启发的概率算法。该算法依赖于根据Boltzmann分布抽取令牌，作为随机抽选方法的扩展和改进。数值结果展示了我们的方法在限制所选输入令牌数量、减少尘埃生成和限制钱包中的令牌池大小方面的有效性。此外，该概率算法可以高效实现，提高性能并尊重隐私要求——这些特性对当前基于令牌的技术具有重要意义。我们比较了Boltzmann抽选与标准随机抽选和贪心算法。我们认为前者在上述目标上优于后者。我们的发现对基于令牌的技术具有相关性，也对CBDC感兴趣，因为作为法定货币，它们可能需要处理大量交易流量。
英文摘要：
Coin selection refers to the problem of choosing a set of tokens to fund a transaction in token-based payment systems such as, e.g., cryptocurrencies or central bank digital currencies (CBDCs). In this paper, we propose the Boltzmann Draw that is a probabilistic algorithm inspired by the principles of statistical physics. The algorithm relies on drawing tokens according to the Boltzmann distribution, serving as an extension and improvement of the Random Draw method. Numerical results demonstrate the effectiveness of our method in bounding the number of selected input tokens as well as reducing dust generation and limiting the token pool size in the wallet. Moreover, the probabilistic algorithm can be implemented efficiently, improves performance and respects privacy requirements - properties of significant relevance for current token-based technologies. We compare the Boltzmann draw to both the standard Random Draw and the Greedy algorithm. We argue that the former is superior to the latter in the sense of the above objectives. Our findings are relevant for token-based technologies, and are also of interest for CBDCs, which as a legal tender possibly needs to handle large transaction volumes at a high frequency.
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