Distributed Price Adjustment Based on Convex Analysis

详细信息    查看全文

• 作者：Yurii Nesterov ; Vladimir Shikhman
• 关键词：Computation of market equilibrium ; Distributed price adjustment ; Convex optimization ; Subgradient methods ; Decentralization of prices
• 刊名：Journal of Optimization Theory and Applications
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：172
• 期：2
• 页码：594-622
• 全文大小：
• 刊物主题：Calculus of Variations and Optimal Control; Optimization; Optimization; Theory of Computation; Applications of Mathematics; Engineering, general; Operation Research/Decision Theory;
• 出版者：Springer US
• ISSN：1573-2878
• 卷排序：172

文摘

In this paper, we suggest a distributed process of price adjustment toward a partial market equilibrium. As the main contribution, our algorithm of price adjustment is computationally efficient and decentralized. Its convergence properties are crucially based on convex analysis. The proposed price adjustment corresponds to a subgradient scheme for minimizing a special nonsmooth convex function. This function is the total excessive revenue of the market’s participants and its minimizers are equilibrium prices. As the main result, the algorithm of price adjustment is shown to converge to equilibrium prices. Additionally, the market clears on average during the price adjustment process, i.e., by historical averages of supply and demand. Moreover, a global rate of convergence is obtained. We endow our algorithm with decentralized prices by introducing the trade design with price initiative of producers. The latter suggests that producers settle and update their individual prices, and consumers buy at the lowest purchase price.