We present a sequential quadratic optimization algorithm for solving nonlinear constrained optimization problems. The unique feature of the algorithm is that inexactness is allowed when solving the arising quadratic subproblems. Easily implementable inexactness criteria for the subproblem solver are established that ensure global convergence guarantees for the overall algorithm. This work represents a step toward scalable active-set methods for large-scale Nonlinear optimization.