(415d) A Data-Driven Inverse Optimization Approach to Learning Surrogate Optimizers
AIChE Annual Meeting
2021
2021 Annual Meeting
Computing and Systems Technology Division
Data-Driven and Hybrid Modeling for Decision Making I
Wednesday, November 10, 2021 - 8:45am to 9:00am
Our work assumes that a parameterized convex optimization model can be trained to obtain optimal solutions that are similar to what the original model generates. Here, the IO problem is to determine the model parameters that minimize the difference between the true optimal solutions and the solutions obtained from solving the resulting learned optimization model. We formulate the bilevel program and apply a penalty block coordinate descent method [4] to solve the single-level KKT-based reformulation of the IO problem. We demonstrate the effectiveness of our method using several case studies including a nonlinear model predictive control example.
References:
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4. Kleinert, T. & Schmidt, M. Computing Feasible Points of Bilevel Problems with a Penalty Alternating Direction Method. INFORMS J. Comput. (2020).