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State-Space Transformation for Approximation and Flexible Computation

  • Weixiong Zhang

Abstract

Most combinatorial optimization problems are NP-hard, and require computation exponential to the problem size. How can we solve difficult tree-search problems approximately, using the analytical results of their average-case complexity? The answer to this question leads to a new approximation approach, the topic of this chapter. This new method makes use of the complexity transitions of branch-and-bound on incremental random trees, and is referred to as ε-transformation.

Keywords

Local Search Goal Node Solution Quality Random Tree Average Relative Error 
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 Science+Business Media New York 1999

Authors and Affiliations

  • Weixiong Zhang
    • 1
  1. 1.Information Sciences Institute and Department of Computer ScienceUniversity of Southern CaliforniaMarina del ReyUSA

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