  Since the 1960s Mastermind has been studied for the combinatorial and
information theoretical interest the game has to offer. Many results have been
discovered starting with Erd\H{o}s and R\'enyi determining the optimal number
of queries needed for two colors. For $k$ colors and $n$ positions, Chv\'atal
found asymptotically optimal bounds when $k \le n^{1-\epsilon}$. Following a
sequence of gradual improvements for $k \geq n$ colors, the central open
question is to resolve the gap between $\Omega(n)$ and $\mathcal{O}(n\log \log
n)$ for $k=n$.
  In this paper, we resolve this gap by presenting the first algorithm for
solving $k=n$ Mastermind with a linear number of queries. As a consequence, we
are able to determine the query complexity of Mastermind for any parameters $k$
and $n$.
