  Wordle is a single-player word-based game where the objective is to guess the
5-letter word in a maximum of 6 tries. The game was released to the public in
October 2021 and has since gained popularity with people competing against each
other to maintain daily streaks and guess the word in a minimum number of
tries. There have been works using probabilistic and reinforcement learning
based approaches to solve the game. Our work aims to formulate and analyze
deterministic algorithms that can solve the game and minimize the number of
turns required to guess the word and do so for any generalized setting of the
game. As a simplifying assumption, for our analysis of all the algorithms we
present, we assume that all letters will be unique in any word which is part of
our vocabulary. We propose two algorithms to play Wordle - one a greedy based
approach, and other based on Cliques. The Greedy approach is applicable for
both hard and easy modes of Wordle, while the Clique formation based approach
only works on the Easy mode. We present our analysis on both approaches one by
one, next.
