一阶近似作为一种Wordle策略
Rank One Approximation as a Strategy for Wordle
摘要：
本文提出了一种玩益智游戏Wordle的数学方法。在Wordle中，玩家有六次尝试来猜测一个秘密单词。在每次猜测之后，玩家会被告知他们的猜测与秘密单词的比较。根据现有的信息，玩家做出下一个猜测。本文提出结合秩一近似和潜在语义索引的矩阵表示的所有可能的解决方案的列表。秩一近似找到词矩阵的主特征向量，潜在语义索引揭示哪个词最接近主特征向量。选择其列向量最接近主特征向量的单词作为下一个猜测。使用这种方法，选择所有可能解决方案的集合中最具代表性的单词。本文介绍了如何将词转换为向量，以及秩一近似和潜在语义索引背后的理论。本文给出的结果表明，与初始猜测的“石板”的方法解决了难题，平均在4.04猜测，与成功率为98.7%
英文摘要：
This paper presents a mathematical method of playing the puzzle game Wordle. In Wordle, the player has six tries to guess a secret word. After each guess the player is told how their guess compares to the secret word. With the available information the player makes their next guess. This paper proposes combining a rank one approximation and latent semantic indexing to a matrix representing the list of all possible solutions. Rank one approximation finds the dominant eigenvector of a matrix of words, and latent semantic indexing reveals which word is closest to the dominant eigenvector. The word whose column vector is closest to the dominant eigenvector is chosen as the next guess. With this method the most representative word of the set of all possible solutions is selected. This paper describes how a word can be converted to a vector and the theory behind a rank one approximation and latent semantic indexing. This paper presents results demonstrating that with an initial guess of "SLATE" the method solves the puzzle in 4.04 guesses on average, with a success rate of 98.7%
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。
