Towards Explainable and Reliable AI Models for Optimization

Abstract

AI and data science have demonstrated remarkable potential in enhancing optimization algorithms. Compared with traditional methods, utilizing AI/ML techniques can potentially offer improvements in aspects like computational speed and solution quality. Despite these advancements, a deep, systematic understanding of these methods remains underdeveloped. In this talk, the speaker will demonstrate through concrete examples how mathematical tools, particularly optimization theory, can be used to unravel the mysteries of these “black boxes.” The speaker will also discuss the development of interpretable and reliable AI models for optimization, grounded in these mathematical principles. 

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