LAUSR

In this paper, we consider linear quadratic team problems with an arbitrary number of quadratic constraints in stochastic and deterministic settings. The team consists of players with different measurements about the state of nature. The objective of the team is to minimize a quadratic cost subject to additional finite number of quadratic constraints. We first consider the problem of a countably infinite number of players in the team for a bounded state of nature with a Gaussian distribution and show that linear decisions are optimal. Then, we consider the problem of team decision problems with additional convex quadratic constraints and show that linear decisions are optimal for the finite and infinite number of players in the team. For the finite player case, the optimal linear decisions can be found by solving a semidefinite program. We then consider the problem of minimizing a quadratic objective for the worst case scenario, subject to an arbitrary number of deterministic quadratic constraints. We show that linear decisions are optimal and can be found by solving a semidefinite program. Finally, we apply the developed theory on dynamic team decision problems in linear quadratic settings.