Replication Data for: An Efficient Structured Perceptron for NP-Hard Combinatorial Optimization Problems

Develop an efficient model of the Structured Perceptron (SP) to learn the objective function of a MOCOP. Evaluation of three techniques to apply the SP on NP-hard optimization problems: 1) using heuristic solving methods during the learning process, 2) solving well-chosen satisfaction variants of the problems, 3) caching solutions computed during the learning process and reusing them. Experiments confirm the validity and speed-ups of these techniques, enabling structured output learning on larger combinatorial problems than before.