The Second-Order T\^atonnement: Decentralized Interior-Point Methods for Market Equilibrium

TL;DR

This paper introduces decentralized second-order t extasciitildeatonnement methods for market equilibrium computation, leveraging best responses to achieve faster convergence with practical scalability in exchange economies.

Contribution

It develops two novel decentralized interior-point methods based on second-order information, improving convergence rates for market equilibrium computation.

Findings

First method achieves O(log(1/ε)) complexity.

Second method attains non-asymptotic superlinear convergence.

Preliminary experiments demonstrate scalability for large problems.

Abstract

The t\^atonnement process and Smale's process are two classical approaches to compute market equilibrium in exchange economies. While the t\^atonnement process can be seen as a first-order method, Smale's process, being second-order, is less popular due to its reliance on additional information from the players and expensive Newton steps. In this paper, we study Fisher exchange market for a broad class of utility functions, where we show that all high-order information required by Smale's process is readily available from players' best responses. Motivated by this observation, we develop two second-order t\^atonnement processes, constructed as decentralized interior-point methods, which are traditionally known to work in a centralized manner. The methods here bear the name "t\^atonnement", since, in spirit, they demand no more information than the classical t\^atonnement process. To address the Newton systems involved, we introduce an explicitly invertible approximation with high-probability guarantees and a scaling matrix that optimally minimizes the condition number, both of which rely solely on best responses as the methods themselves. Using these tools, the first second-order t\^atonnement process has O(log(1/$\epsilon$))complexity rate. Under mild conditions, the other method achieves a non-asymptotic superlinear convergence rate. Preliminary experiments are presented to justify the capability of the proposed methods for large-scale problems. Extensions of our approach are also discussed.