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This paper establishes the existence of team-optimal solutions in certain classes of static teams with observation sharing information structures. To establish the existence result, we first construct an equivalent virtual static team (or multiple virtual static teams) with no observation sharing information structure. Then, we establish the existence of a team-optimal solution in the virtual static team, thereby establishing the existence result for the original team. We use the existence result to show that Witsenhausen's counterexample with a certain kind of side information admits a team-optimal solution. [ABSTRACT FROM AUTHOR]

In this paper, we identify sufficient conditions under which static teams and a class of sequential dynamic teams admit team-optimal solutions. We first investigate the existence of optimal solutions in static teams where the observations of the decision makers are conditionally independent given the state and satisfy certain regularity conditions. Building on these findings and the static reduction method of Witsenhausen, we then extend the analysis to sequential dynamic teams. In particular, we show that a large class of dynamic linear-quadratic-Gaussian (LQG) teams, including the vector version of the well-known Witsenhausen's counterexample and the Gaussian relay channel problem viewed as a dynamic team, admit team-optimal solutions. Results in this paper substantially broaden the class of stochastic control problems with nonclassical information known to have optimal solutions. [ABSTRACT FROM AUTHOR]

Synthesis of optimal controllers achieving finite-time consensus for multiagent networks described by fixed or periodic connectivity graphs is considered. The solution procedure involves posing a partially nested, finite-horizon decentralized control problem and converting it to a static convex optimization problem by a quadratic invariance argument. The dynamic feedback controller thus synthesized optimizes a transient performance measure and guarantees consensus within a minimal number of steps. [ABSTRACT FROM AUTHOR]

In this paper, we extend the notion of person-by-person (pbp) optimization to binary decision spaces. The novelty of our approach is the adaptation to a dynamic team context of notions borrowed from the pseudo-boolean optimization field as completely local-global or unimodal functions and submodularity. We also generalize the concept of pbp optimization to the case where groups of m decisions makers make joint decisions sequentially, which we refer to as mbm optimization. The main contribution is a description of sufficient conditions, verifiable in polynomial time, under which a pbp or an mbm optimization algorithm converges to the team-optimum. As a second contribution, we present a local and greedy algorithm characterized by approximate decision strategies (i.e., strategies based on a local state vector) that return the same decisions as in the complete information framework (where strategies are based on full state vector). As a last contribution, we also show that there exists a subclass of submodular team problems, recognizable in polynomial time, for which the pbp optimization converges for at least an opportune initialization of the algorithm. [ABSTRACT FROM AUTHOR]

In this paper we study a discrete version of the classical team decision problem. It has been shown previously that the general discrete team decision problem is NP-hard. Here we present an efficient approximation algorithm for this problem. For the maximization version of this problem with nonnegative rewards, this algorithm computes decision rules which are guaranteed to be within a fixed bound of optimal. [ABSTRACT FROM AUTHOR]

In this paper we discuss a class of team problems with discrete action spaces. We introduce multimodularity into team theory as a natural alternative to convexity in continuous spaces. The main result relates coordinatewise-optimal (cw-optimal) points to the optimal team decision for a class of team problems. The method is based on a characterization of coordinatewise minima of multimodular functions. [ABSTRACT FROM AUTHOR]