论文标题
将一个近似值作为文字的策略
Rank One Approximation as a Strategy for Wordle
论文作者
论文摘要
本文提出了一种玩拼图游戏Wordle的数学方法。在Wordle中，玩家有六次试图猜测一个秘密单词。每次猜测之后，玩家都会被告知他们的猜测与秘密单词相比。有了可用的信息，玩家将进行下一个猜测。本文提出，将等级的近似值和潜在语义索引结合到代表所有可能解决方案列表的矩阵。等级的近似值发现单词矩阵的主要特征向量，而潜在的语义索引揭示了哪个单词最接近主导的特征向量。将其列向量最接近的单词选择作为下一个猜测。使用此方法，选择了所有可能解决方案的集合中最具代表性的单词。本文介绍了一个单词如何转换为向量以及等级近似和潜在语义索引背后的理论。本文提出的结果表明，该方法的最初猜测平均猜测为4.04，成功率为98.7％。
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%
