Machine Learning-Powered Optimization

When an optimization solver is asked to solve a problem, it needs to consider several aspects of the problem and make decisions on how it would approach solving it. Key algorithmic decisions must be made before and during the search for optimal solutions. These algorithmic decisions can have a big impact on speed and numerical stability. Machine learning techniques are now ubiquitous. But, do you know that they are being used within FICO® Xpress Solver to make those key algorithmic choices? Join us in this talk to learn about how Xpress Solver employs machine learning to improve numerical robustness and performance.