Wheeler DFAs (WDFAs) are a sub-class of finite-state automata which is playing an important role in the emerging field of \emph{compressed data structures}: as opposed to general automata, WDFAs can be stored in just $\log\sigma + O(1)$ bits per edge, $\sigma$ being the alphabet's size, and support optimal-time pattern matching queries on the substring closure of the language they recognize. An important step to achieve further compression is minimization. When the input $\mathcal A$ is a general deterministic finite-state automaton (DFA), the state-of-the-art is represented by the classic Hopcroft's algorithm, which runs in $O(|\mathcal A|\log |\mathcal A|)$ time. This algorithm stands at the core of the only existing minimization algorithm for Wheeler DFAs, which inherits its complexity. In this work, we show that the minimum WDFA equivalent to a given input WDFA can be computed in linear $O(|\mathcal A|)$ time.
When run on de Bruijn WDFAs built from real DNA datasets, an implementation of our algorithm reduces the number of nodes from 14\% to 51\% at a speed of more than 1 million nodes per second.