  This paper proposes a fast Markov Matrix-based methodology for computing Top
Trading Cycles (TTC) that delivers O(1) computational speed, that is speed
independent of the number of agents and objects in the system. The proposed
methodology is well suited for complex large-dimensional problems like housing
choice. The methodology retains all the properties of TTC, namely,
Pareto-efficiency, individual rationality and strategy-proofness.
