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Learning the Empirical Hardness of Optimization Problems: The Case of Combinatorial Auctions

  • Kevin Leyton-Brown
  • Eugene Nudelman
  • Yoav Shoham
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2470)

Abstract

We propose a new approach for understanding the algorithm-specific empirical hardness of Open image in new window -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.

Keywords

Root Mean Square Error Problem Instance Cluster Coefficient Edge Density Subset Selection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Kevin Leyton-Brown
    • 1
  • Eugene Nudelman
    • 1
  • Yoav Shoham
    • 1
  1. 1.Computer Science DepartmentStanford UniversityStanford

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