Barrier methods have led to several nonlinear programming (NLP) solvers (e.g. IPOPT, KNITRO, LOQO). However, certain regularity conditions are required for convergence of these methods. These conditions are violated for optimization models with dependent constraints, thus leading to method failure. These shortcomings can be identified by checking the inertia of the KKT matrix, and current solvers either add regularizing terms to correct the inertia of the KKT matrix or revert to more expensive trust region methods to solve the barrier problem. This study improves on these approaches with a new structured regularization strategy; within the Newton step it identifies an independent subset of equality constraints and removes the remaining constraints without modifying the KKT matrix structure. This approach leads to more accurate Newton steps and faster convergence, while maintaining global convergence properties. Implemented in IPOPT with linear solvers HSL_MA57, HSL_MA97 and MUMPS, we present numerical experiments on hundreds of examples from the CUTEr test set, modified for dependency. These results show an average reduction in iterations of more than 50 % over the current version of IPOPT. In addition, several nonlinear blending problems are solved with the proposed algorithm, and improvements over existing regularization strategies are further demonstrated.
['Wei Wan', 'Lorenz T. Biegler']
Structured regularization for barrier NLP solvers