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Ellipsoidal one-class constraint acquisition for quadratically constrained programming
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DOI：10.1016/j.ejor.2020.12.018.png)
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Abstract
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We propose Ellipsoidal One-Class Constraint Acquisition (EOCCA), a fast and scalable algorithm for the acquisition of constraints for Mixed-Integer Quadratically Constrained Programming (MIQCP) models from data. EOCCA acquires a well-formed MIQCP model using solely the examples of the feasible solutions to this model. It combines x-means partitioning, standardization, and principal components analysis to preprocess the training set and then wraps the preprocessed data into several hyper-ellipsoids expressed using MIQCP constraints. These MIQCP constraints are projected back to the space of the original training set, and their further use does not require data preprocessing. Experimental evaluation shows that EOCCA scores better than a state-of-the-art algorithm in terms of fidelity of the acquired constraints to ground-truth constraints and achieves this in few orders of magnitude shorter time. We demonstrate the practical use case of EOCCA in a fully automated workflow of modeling and optimization of a rice farm using real-world data. (C) 2020 Elsevier B.V. All rights reserved.
Keywords:
Artificial intelligence
Quadratic programming
System modeling
Process mining
Business process
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