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.

翻译：在存在第一和第二衍生物的地方,包括制约因素在某种解决办法上可能不线性独立的问题(即无法满足LICQ),Alogorithm NCCL是针对一般的平稳优化问题设计的。它相当于LanceLOT扩大Lagrangian方法,改写为非线性限制的子问题短序,可由IPOPT和KNITRO有效解决,每个子问题都有温暖的开端。我们从Julia执行Algorithm NCL关于不符合LICQ的税收政策模式以及非线性最低问题和CUnest测试集的一般问题中得出数字结果。