Machine Learning for Optimization

Ade Fajemisin and Donato Maragno on recent trends and challenges in applying ML to optimization problems.

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This week’s guests are Ade Fajemisin (Postdoctoral Researcher) and Donato Maragno (PhD Student) of the University of Amsterdam. They were co-authors of a recent paper (“Optimization with Constraint Learning: A Framework and Survey”) that explores how machine learning can be used to learn constraints in optimization problems. Our conversation focused on key findings in their paper, as well as trends in the use of machine learning for optimization problems.

Image: Machine Learning for Optimization by Ade Fajemisin.

Optimization problems are routine in many industries including logistics, manufacturing, retail, energy, and financial services. Such problems can often be complex and hard to solve in practice, and as such teams often have to solve simpler and perhaps less realistic optimization tasks. In previous episodes, I’ve had guests describe how reinforcement learning can be used to tackle large-scale simulation and optimization problems.

Highlights in the video version:

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[Image: Dell Rapids grain elevator by Ben Lorica.]