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.