  We introduce the concept of decision-focused surrogate modeling for solving
computationally challenging nonlinear optimization problems in real-time
settings. The proposed data-driven framework seeks to learn a simpler, e.g.
convex, surrogate optimization model that is trained to minimize the decision
prediction error, which is defined as the difference between the optimal
solutions of the original and the surrogate optimization models. The learning
problem, formulated as a bilevel program, can be viewed as a data-driven
inverse optimization problem to which we apply a decomposition-based solution
algorithm from previous work. We validate our framework through numerical
experiments involving the optimization of common nonlinear chemical processes
such as chemical reactors, heat exchanger networks, and material blending
systems. We also present a detailed comparison of decision-focused surrogate
modeling with standard data-driven surrogate modeling methods and demonstrate
that our approach is significantly more data-efficient while producing simple
surrogate models with high decision prediction accuracy.
