Methods for sequential decision making are often built upon a foundational assumption that the underlying decision process is stationary [Sutton and Barto, 2018]. While this assumption was a cornerstone when laying the theoretical foundations of the field, and while is often reasonable, it isseldom trueinpractice andcanbeunreasonable [Dulac-Arnold etal.,2019].

Largescale instances of SDPs, thus, present a memory bottleneck. In this paper, we develop simple polynomial-time Gaussian sampling-based algorithms for these twoproblems thatuseO(n+|E|)memory andnearly achievethebestexisting approximation guarantees.