How machine learning meets optimization

Machine learning and constraint-based optimization are both used to solve critical business problems. They come from distinct research communities and have traditionally been treated separately. But Jari Koister examines how they're similar, how they're different, and how they can be used to solve complex problems with amazing results.

Optimization and ML are increasingly intersecting, occasionally overlapping, sometimes complementary, and most often best used in combination. Data scientists should be interested in operations research, and operations researchers are increasingly using machine learning. Jari Koister dives into understanding and applying these two techniques. He explores when optimization techniques originating from operations research are the better solution and when it’s beneficial to apply ML. More importantly, he outlines how complex, high-value business problems can be better solved by combining the techniques rather than by using only one of them. People struggle to describe how they relate from a theoretical perspective, if ML is just an optimization problem, or how simplex, MIP, interior point, neural networks, and gradient decent relate. Jari outlines a model that helps you understand how the two techniques are related, overlap, and differ.