Amortized Analysis via Coalgebra

Amortized analysis is a cost analysis technique for data structures in which cost is studied in aggregate, rather than considering the maximum cost of a single operation. Traditionally, amortized analysis has been phrased inductively, in terms of finite sequences of operations. Connecting to prior work on coalgebraic semantics for data structures, we develop the perspective that amortized analysis is naturally viewed coalgebraically in the category of algebras for a cost monad, where a morphism of coalgebras serves as a first-class generalization of potential function suitable for integrating cost and behavior. Using this simple definition, we consider amortization of other effects, such as randomization, and we compose amortization arguments in the indexed category of coalgebras. We generalize this to parallel data structure usage patterns by using coalgebras for an endoprofunctor instead of an endofunctor, combining potential using a monoidal structure on the underlying category. Finally, we adapt our discussion to the bicategorical setting, supporting imprecise amortized upper bounds.