(575b) Reduced Space Formulation for Global Optimization with Gaussian Processes (Kriging) Embedded
In process systems engineering, GPs are commonly used to emulate unit operations or complete flowsheets from data. Subsequently, a hybrid data-driven/mechanistic (flowsheet) model with trained GPs embedded is optimized. Most of the previous works on optimizations with GPs embedded rely on local optimization techniques (e.g., [3,4]). Deterministic global optimization of problems with GPs embedded was performed by Cozad et al. (2014) , Boukouvala et al. (2017) , and Keßler et al. (2019)  using the state-of-the-art solvers BARON and ANTIGONE. However, these works observed that GPs satisfy accuracy requirements but are difficult-to-solve for deterministic global solvers [9,10]. In our previous work, we overcame similar problems when artificial neural networks are embedded in optimization problems using a reduced-space formulation .
Herein, we present a method for deterministic global optimization of optimization problems with GPs embedded in a reduced variable space. In particular, we propagate McCormick relaxations through GPs [13,14]. This method allows the branch-and-bound algorithms to operate only on the degrees of freedom and hides the complex GP structure. The optimization problems with GPs embedded are solved by our in-house global deterministic solver MAiNGO . The performance and scaling of the method are demonstrated on a set of illustrative case studies. The results show that the reduced-space method reduces the problem size significantly compared to the full-space formulation. The proposed method performs favorably compared BARON and allows efficient deterministic global optimization with (trained) GPs embedded.
Acknowledgment: The authors gratefully acknowledge the financial support of the Kopernikus project SynErgie by the Federal Ministry of Education and Research (BMBF) and the project supervision by the project management organization Projektträger Jülich (PtJ).
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