We propose a nonlinear additive Schwarz method for solving nonlinear
optimization problems with bound constraints. Our method is used as a
"right-preconditioner" for solving the first-order optimality system arising
within the sequential quadratic programming (SQP) framework using Newton's
method. The algorithmic scalability of this preconditioner is enhanced by
incorporating a solution-dependent coarse space, which takes into account the
restricted constraints from the fine level. By means of numerical examples, we
demonstrate that the proposed preconditioned Newton methods outperform standard
active-set methods considered in the literature