activity
19992022
most citedA static cost analysis for a higher-order language

31 citations · 92 across the 14 of their papers we have counts for

collaborators

14 papers

cs.PL2022

Recurrence extraction and denotational semantics with recursive definitions

Norman Danner

With one exception, our previous work on recurrence extraction and denotational semantics has focused on a source language that supports inductive types and structural recursion. T…

cs.CC2022

General Ramified Recurrence and Polynomial-time Completeness

Norman Danner, James S. Royer

We exhibit a sound and complete implicit-complexity formalism for functions feasibly computable by structural recursions over inductively defined data structures. Feasibly computab…

cs.PL2020★ 13 cited

Denotational recurrence extraction for amortized analysis

Joseph W. Cutler, Daniel R. Licata, Norman Danner

A typical way of analyzing the time complexity of functional programs is to extract a recurrence expressing the running time of the program in terms of the size of its input, and t…

cs.PL2020★ 3 cited

Denotational semantics as a foundation for cost recurrence extraction for functional languages

Norman Danner, Daniel R. Licata

A standard informal method for analyzing the asymptotic complexity of a program is to extract a recurrence that describes its cost in terms of the size of its input, and then to co…

cs.PL2019★ 20 cited

Recurrence Extraction for Functional Programs through Call-by-Push-Value (Extended Version)

G. A. Kavvos, Edward Morehouse, Daniel R. Licata +1

The main way of analyzing the complexity of a program is that of extracting and solving a recurrence that expresses its running time in terms of the size of its input. We develop a…

cs.PL2015

Denotational cost semantics for functional languages with inductive types

Norman Danner, Daniel R. Licata, Ramyaa Ramyaa

A central method for analyzing the asymptotic complexity of a functional program is to extract and then solve a recurrence that expresses evaluation cost in terms of input size. Th…