This document describes the GAMS/PATHNLP solver for non-linear programs and the options unique to this solver. PATHNLP solves an NLP by internally constructing the Karush-Kuhn-Tucker (KKT) system of first-order optimality conditions associated with the NLP and solving this system using the PATH solver for complementarity problems. The solution to the original NLP is extracted from the KKT solution and returned to GAMS. All of this takes place automatically - no special syntax or user reformulation is required. Typically, PATHNLP works very well for convex models. It also has a comparative advantage on models whose solution via reduced gradient methods results in a large number of superbasic variables, since the PATH solver won’t construct a dense reduced Hessian in the space of the superbasic variables as reduced gradient solvers do. For nonconvex models, however, PATHNLP is not as robust as the reduced gradient methods. The theory relating NLP to their KKT systems is well-known: assuming differentiability without convexity, and assuming a constraint qualification holds, then a solution to the NLP must also be a solution to the KKT system. If we also assume convexity, then a solution to the KKT system is also a solution to the NLP - no further constraint qualification is required. In case PATH fails to find a solution to the KKT system for the NLP, a phase I / phase II method is used in which the phase I objective is simply the feasibility error and the original objective is ignored. If a feasible point is found in phase I then phase II, an attempt to solve the KKT system for the NLP using the current feasible point, is entered. PATHNLP is installed automatically with your GAMS system. Without a license, it will run in student or demonstration mode (i.e. it will solve small models only). If your GAMS license includes PATH, this size restriction is removed