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%

翻译：本文展示了玩拼图游戏 Wordle 的数学方法。 在 Wordle 中, 玩家有 6 个试图猜一个秘密单词 。 每个猜想的玩家都被告知他们的猜想如何与秘密单词比较。 每个玩家下一个猜想。 有了可用的信息, 玩家将进行下一个猜想。 本文建议将一个排序的近似和潜在语义索引合并到代表所有可能解决方案列表的矩阵中。 排序近似将找到一个单词矩阵的主要导体, 和潜在语义索引显示哪个词最接近主向导。 下一个猜想是哪个单词的柱体最接近主向导的单词。 下一个猜想是选择了该单词。 使用这个方法, 选择了所有可能解决方案集中最具代表性的单词 。 本文描述了如何将单词转换为矢量, 以及一个排序的近似和潜在语义索引背后的理论。 本文展示了结果, 最初的猜想“ SLATE” 方法在4. 4 猜想中解决了谜题,, 成功率为98. 。