| Abstrakt: |
Traditional deterministic global optimization methods are often based on a Branch-and-Bound (BB) search tree, which may grow rapidly, preventing the method to find a good solution. Motivated by decomposition-based inner approximation (column generation) methods for solving transport scheduling problems with over 100 million variables, we present two new deterministic decomposition-based successive approximation methods for general modular and/or sparse MINLPs. The first algorithm, called DECOA, is a successive MIP-outer-approximation algorithm based on refining nonconvex polyhedral underestimators of nonlinear functions. The second algorithm, called DIOR, is based on successively improving inner and outer approximations by solving column and cut generation sub-problems using DECOA. Both algorithms are part of Decogo, a new parallel MINLP solver. We describe the basic ideas of both algorithms, and present numerical results with Decogo for instances of the MINLPlib2. [ABSTRACT FROM AUTHOR] |
