  In this paper, we analyze a derivative-free line search method designed for
bound-constrained problems. Our analysis demonstrates that this method exhibits
a worst-case complexity comparable to other derivative-free methods for
unconstrained and linearly constrained problems. In particular, when minimizing
a function with $n$ variables, we prove that at most ${\cal O}(n\epsilon^{-2})$
iterations are needed to drive a criticality measure below a predefined
threshold $\epsilon$, requiring at most ${\cal O}(n^2\epsilon^{-2})$ function
evaluations. We also show that the total number of iterations where the
criticality measure is not below $\epsilon$ is upper bounded by ${\cal
O}(n^2\epsilon^{-2})$. Moreover, we investigate the method capability to
identify active constraints at the final solutions. We show that, after a
finite number of iterations, all the active constraints satisfying the strict
complementarity condition are correctly identified.
