  Algorithm NCL is designed for general smooth optimization problems where
first and second derivatives are available, including problems whose
constraints may not be linearly independent at a solution (i.e., do not satisfy
the LICQ). It is equivalent to the LANCELOT augmented Lagrangian method,
reformulated as a short sequence of nonlinearly constrained subproblems that
can be solved efficiently by IPOPT and KNITRO, with warm starts on each
subproblem. We give numerical results from a Julia implementation of Algorithm
NCL on tax policy models that do not satisfy the LICQ, and on nonlinear
least-squares problems and general problems from the CUTEst test set.
