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A Knapsack-Based Approach to Bidding in Ad Auctions
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We model the problem of bidding in ad auctions as a penalized multiple choice knapsack problem (PMCKP), a combination of the multiple choice knapsack problem (MCKP) and the penalized knapsack problem (PKP) [1]. We present two versions of PMCKPGlobalPMCKP and LocalPMCKP, together with a greedy algorithm that solves the linear relaxation of a GlobalPMCKP optimally. We also develop a greedy heuristic for solving LocalPMCKP. Although our heuristic is not optimal, we show that it performs well in TAC AA games.
Title: A Knapsack-Based Approach to Bidding in Ad Auctions
Description:
We model the problem of bidding in ad auctions as a penalized multiple choice knapsack problem (PMCKP), a combination of the multiple choice knapsack problem (MCKP) and the penalized knapsack problem (PKP) [1].
We present two versions of PMCKPGlobalPMCKP and LocalPMCKP, together with a greedy algorithm that solves the linear relaxation of a GlobalPMCKP optimally.
We also develop a greedy heuristic for solving LocalPMCKP.
Although our heuristic is not optimal, we show that it performs well in TAC AA games.
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