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In this work, we investigate the distributed generalized Nash equilibrium(GNE) seeking problems for $N$-coalition games with inequality constraints.First, we study the scenario where each agent in a coalition has fullinformation of all the constraints. A finite-time average consensus-basedapproach is proposed by using full decision information. It is proven that thealgorithm converges to a GNE (specifically, a variational equilibrium) of thegame. Then, based on the finite-time consensus tracking, we propose adistributed algorithm by using partial decision information only, where onlyneighboring action information is needed for each agent. Furthermore, weinvestigate the scenario where only distributive constraint information isavailable for the agents. The terminology ``distributive" here refers to thescenario where the constraint information acquisition and processing areconducted across multiple agents in a coalition, and each agent has access toonly a subset of constraints, instead of the scenario where each agent has fullinformation of all the constraints. In this case, the multiplier computationand the constraint information required for each individual are reduced. A fulldecision information algorithm and a partial decision information algorithm forthis scenario are proposed, respectively. Numerical examples are given toverify the proposed algorithms.

Distributed Generalized Nash Equilibrium Seeking of N-Coalition Games with Full and Distributive Constraints