Abstract
In this paper, we model and formulate the search-based advertising auction problem with multiple slots, choice behaviors of advertisers, and the popular generalized second-price mechanism. A Lagrangian-based method is then proposed for tackling this problem. We present an extension to the subgradient algorithm based on Lagrangian relaxation coupled with the column generation method in order to improve the dual multipliers and accelerate its convergence. Simulation results show that the proposed algorithm is efficient and it shows significant improvement compared to the greedy algorithm.
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