Abstract
We propose a new approach for understanding the algorithm-specific empirical hardness of -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.
We would like to acknowledge contributions by Rámon Béjar, Carla Gomes, Henry Kautz, Bart Selman, Lyle Ungar and Ioannis Vetsikas.
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Leyton-Brown, K., Nudelman, E., Shoham, Y. (2002). Learning the Empirical Hardness of Optimization Problems: The Case of Combinatorial Auctions. In: Van Hentenryck, P. (eds) Principles and Practice of Constraint Programming - CP 2002. CP 2002. Lecture Notes in Computer Science, vol 2470. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46135-3_37
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DOI: https://doi.org/10.1007/3-540-46135-3_37
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