The variables are classified as basic , superbasic , and nonbasic , with the number of superbasics indicating the effective nonlinearity of the objective. The constraints are satisfied before the objective is evaluated.
Feasibility is maintained thereafter.
Search directions are generated using a quasi-Newton approximation to the reduced Hessian. A projected augmented Lagrangian algorithm is used. As in Robinson's method, each major iteration solves a linearly constrained subproblem to generate a search direction. The subproblem objective is an augmented Lagrangian function. The subproblem constraints are the true linear constraints plus linearizations of the nonlinear constraints.