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JACIII

2007

2007

We study the problem of approximating pseudoBoolean functions by linear pseudo-Boolean functions. Pseudo-Boolean functions generalize ordinary Boolean functions by allowing the function values to be real numbers instead of just the 0-1 values. Pseudo-Boolean functions have been used by AI and theorem proving researchers for eﬃcient constraint satisfaction solving. They can also be applied for modeling uncertainty. We investigate the possibility of eﬃciently computing a linear approximation of a pseudo-Boolean function of arbitrary degree. We show some example cases in which a simple (efﬁciently computable) linear approximating function works just as well as the best linear approximating function, which may require exponential amount of computation to obtain. We conjecture that for any pseudo-Boolean function of ﬁxed degree k > 1 where k is independent of the number of Boolean variables, the best linear approximating function works better than simply using the linear part of...

Related Content

Added |
15 Dec 2010 |

Updated |
15 Dec 2010 |

Type |
Journal |

Year |
2007 |

Where |
JACIII |

Authors |
Guoli Ding, Robert F. Lax, Peter P. Chen, Jianhua Chen |

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