We propose a new approach for understanding the algorithm specific empirical hardness of NP-Hard problems. In this work we focus on the empirical hardness of the winner determination problem—an optimization problem arising in combinatorial auctions—when solved by ILOG’s CPLEX software. We consider nine widely-used problem distributions and sample randomly from a continuum of parameter settings for each distribution. We identify a large number of distribution-nonspecific features of data instances and use statistical regression techniques to learn, evaluate and interpret a function from these features to the predicted hardness of an instance.
CITATION STYLE
Leyton-Brown, K., Nudelman, E., & Shoham, Y. (2002). Learning the empirical hardness of optimization problems: The case of combinatorial auctions. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2470, pp. 556–572). Springer Verlag. https://doi.org/10.1007/3-540-46135-3_37
Mendeley helps you to discover research relevant for your work.