Constraint-Based Optimization with the Minimax Decision Criterion

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Abstract. In many situations, a set of hard constraints encodes the feasible conﬁgurations of some system or product over which users have preferences. We consider the problem of computing a best feasible solution when the user’s utilities are partially known. Assuming bounds on utilities, efﬁcient mixed integer linear programs are devised to compute the solution with minimax regret while exploiting generalized additive structure in a user’s utility function.

Craig Boutilier, Relu Patrascu, Pascal Poupart, Da

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CP 2003 | Feasible Conﬁgurations | Mixed Integer Linear | Programming Languages | User’s Utility Function |

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Added	06 Jul 2010
Updated	06 Jul 2010
Type	Conference
Year	2003
Where	CP
Authors	Craig Boutilier, Relu Patrascu, Pascal Poupart, Dale Schuurmans

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Constraint-Based Optimization with the Minimax Decision Criterion

CP 2003 | Feasible Conﬁgurations | Mixed Integer Linear | Programming Languages | User’s Utility Function |

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