Abstract: Systems and methods that extract features from a set of optimization problems, and compile performance characteristics of optimization algorithms that are applied to each optimization problem. Machine learning models are trained on a first portion of a dataset that comprises the features and performance characteristics. A model is selected based on performance on a second portion of the dataset. The selected model is applied to features of a new optimization problem to provide performance characteristics of each optimization algorithm, which can then be ranked based on the respective performance characteristics. Either the first-ranked optimization algorithm can be applied to the new optimization problem, or successively-ranked optimization algorithms can be executive iteratively.