An alternating algorithm for finding linear arrow-debreu market equilibria

Po-An Chen, Chi-Jen Lu, Yu-Sin Lu

    Research output: Contribution to journalArticlepeer-review


    Motivated by the convergence result of mirror-descent algorithms to market equilibria in linear Fisher markets, it is natural for one to consider designing dynamics (specifically, iterative algorithms) for agents to arrive at linear Arrow-Debreu market equilibria. Jain reduced equilibrium computation in linear Arrow-Debreu markets to the equilibrium computation in bijective markets, where everyone is a seller of only one good and a buyer for a bundle of goods. In this paper, we design an algorithm for computing linear bijective market equilibrium, based on solving the rational convex program formulated by Devanur et al. The algorithm repeatedly alternates between a step of gradient-descent-like updates and a distributed step of optimization exploiting the property of such convex program. Convergence can be ensured by a new analysis different from the analysis for linear Fisher market equilibria.
    Original languageEnglish
    Number of pages13
    JournalTheory of Computing Systems
    StatePublished - Jul 2021


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