This paper describes the automated complexity analysis (ACA) system for automated higherorder complexity analysis of functional programs synthesized with the NUPRL proof development system. We introduce a general framework for deÿning models of computational complexity for functional programs based on an annotation of a given operational language semantics. Within this framework, we use type decomposition and polynomialization to express the complexity of higher-order terms. Symbolic interpretation of open terms automates complexity analysis, which involves generating and solving higher-order recurrence equations. Finally, the use of the ACA system is demonstrated by analyzing three di erent implementations of the pigeonhole principle.
Benzinger REF developed an automated complexity analysis for functional programs synthesized using the Nuprl proof system.
Automated higher-order complexity analysis
Theor. Comput. Sci.
Theor. Comput. Sci.
Mathematics Computer Science