Alternative Regularizations for OA Algorithms for Convex MINLP

In this work, we extend the regularization framework from Kronqvist et al. ( by incorporating several new regularization functions and develop a regularized single-tree search method for solving convex mixed-integer nonlinear programming (MINLP) problems. We propose a set of regularization functions based on distance-metrics and Lagrangean approximations, used in the projection problem for finding new integer combinations to be used within the Outer-Approximation (OA) method. The new approach, called Regularized Outer-Approximation (ROA), has been implemented as part of the open-source Mixed-integer nonlinear decomposition toolbox for Pyomo - MindtPy. We compare the OA method with seven regularization function alternatives for ROA. Moreover, we extend the LP/NLP Branch & Bound method proposed by Quesada and Grossmann to include regularization in an algorithm denoted RLP/NLP. We provide convergence guarantees for both ROA and RLP/NLP. Finally, we perform an extensive computational experiment by considering all convex MINLP problems in the benchmark library MINLPLib. The computational results show clear advantages of using regularization in combination with the OA method.



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