METHOD AND SYSTEM FOR HYPERPARAMETER AND ALGORITHM SELECTION FOR MIXED INTEGER LINEAR PROGRAMMING PROBLEMS USING REPRESENTATION LEARNING

摘要

A method for hyperparameter selection (HPS) and algorithm selection (AS) for mixed integer linear programming (MILP) problems includes collecting MILP problems and performances of associated solvers for optimizing the MILP problems. Each of the MILP problems is mapped into a graph having nodes each comprising one of the variables and constraints of the MILP problems. Raw features of the nodes of the graphs are generated. For each of the graphs, a representation of the nodes of the graphs is learned using the raw features which is global to the MILP problems using the raw features. A machine learning model is trained using the learned representations. The trained learning model is used to select one of the solvers for a new MILP problem.