arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1607.04710cs.GT
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匹配市场的降价拍卖

A Descending Price Auction for Matching Markets

Shih-Tang Su, Jacob D. Abernethy, Grant Schoenebeck, Vijay G. Subramanian

摘要:

本文提出了一种降价拍卖算法,利用组合结构,在单位需求匹配市场中获得最大的市场出清价格向量(MCP)。通过精明地选择每一步都降低价格的商品,该算法只使用组合结构,避免了求解LP,并且具有$O(m^4)$的强多项式运行时间。该算法的关键是确定一组需求不足的商品,我们同时降低价格在算法的每一步。这是通过选择使偏度函数最大化的商品子集来实现的,这使得二分图序列在$O(m^2)$步骤中收敛到最大MCP处的组合结构。提出了一种图着色算法,用于寻找具有最大偏斜度值的商品集合,从而产生$O(m^4)$复杂度。

英文摘要:

This work presents a descending-price-auction algorithm to obtain the maximum market-clearing price vector (MCP) in unit-demand matching markets with m items by exploiting the combinatorial structure. With a shrewd choice of goods for which the prices are reduced in each step, the algorithm only uses the combinatorial structure, which avoids solving LPs and enjoys a strongly polynomial runtime of $O(m^4)$. Critical to the algorithm is determining the set of under-demanded goods for which we reduce the prices simultaneously in each step of the algorithm. This we accomplish by choosing the subset of goods that maximize a skewness function, which makes the bipartite graph series converges to the combinatorial structure at the maximum MCP in $O(m^2)$ steps. A graph coloring algorithm is proposed to find the set of goods with the maximal skewness value that yields $O(m^4)$ complexity.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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