Condition numbers of stochastic mean payoff games and what they say about nonarchimedean semidefinite programming

TL;DR

This paper links the mean payoff of stochastic games to a condition number for nonarchimedean semidefinite feasibility, providing geometric insights and complexity bounds for solving these problems.

Contribution

It introduces a novel interpretation of the mean payoff as a condition number and establishes bounds on problem complexity based on this metric.

Findings

Mean payoff equals a condition number measuring feasibility proximity.

The condition number corresponds to the maximal radius in Hilbert's projective metric.

Value iteration has pseudopolynomial complexity for fixed random positions.

Abstract

Semidefinite programming can be considered over any real closed field, including fields of Puiseux series equipped with their nonarchimedean valuation. Nonarchimedean semidefinite programs encode parametric families of classical semidefinite programs, for sufficiently large values of the parameter. Recently, a correspondence has been established between nonarchimedean semidefinite programs and stochastic mean payoff games with perfect information. This correspondence relies on tropical geometry. It allows one to solve generic nonarchimedean semidefinite feasibility problems, of large scale, by means of stochastic game algorithms. In this paper, we show that the mean payoff of these games can be interpreted as a condition number for the corresponding nonarchimedean feasibility problems. This number measures how close a feasible instance is from being infeasible, and vice versa. We show that it coincides with the maximal radius of a ball in Hilbert's projective metric, that is included in the feasible set. The geometric interpretation of the condition number relies in particular on a duality theorem for tropical semidefinite feasibility programs. Then, we bound the complexity of the feasibility problem in terms of the condition number. We finally give explicit bounds for this condition number, in terms of the characteristics of the stochastic game. As a consequence, we show that the simplest algorithm to decide whether a stochastic mean payoff game is winning, namely value iteration, has a pseudopolynomial complexity when the number of random positions is fixed.