GPU-accelerated semidefinite programming for causal games
This paper presents a GPU-accelerated semidefinite programming solver that enables the exploration of higher local dimensions in causal games, revealing that increasing the dimension beyond does not significantly improve the winning probability, thereby suggesting that current strategies are insufficient to close the gap with known upper bounds.