  In this paper, we consider linear quadratic team problems with an arbitrary
number of quadratic constraints in both 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 will first consider the
Gaussian case, where the state of nature is assumed to have a Gaussian
distribution, and show that the linear decisions are optimal and 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 can be
found by solving a semidefinite program.
