The paper presents CONOPT, an optimization system for static and dynamic large scale nonlinearly constrained optimization problems. The system is based on the GRG algorithm. All computations involving the Jacobian of the constraints use sparse matrix algorithm from linear programming modified to take optimal advantage of the nonlinearity. The paper presents the main features of the system, especially the inversion routines and their data structures, the dynamic setting of tolerances in Newton's algorithm, and the user features in the overall packaging. Computational experience with some medium to large models is presented.