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.
Local Search Goal Node Solution Quality Random Tree Average Relative Error
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