Two algorithms for maintaining order in a list
STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
The essence of functional programming
POPL '92 Proceedings of the 19th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
PLDI '94 Proceedings of the ACM SIGPLAN 1994 conference on Programming language design and implementation
Monad transformers and modular interpreters
POPL '95 Proceedings of the 22nd ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Cayenne—a language with dependent types
ICFP '98 Proceedings of the third ACM SIGPLAN international conference on Functional programming
Adaptive functional programming
POPL '02 Proceedings of the 29th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Generic unification via two-level types and parameterized modules
Proceedings of the sixth ACM SIGPLAN international conference on Functional programming
The Definition of Standard ML
An experimental analysis of self-adjusting computation
Proceedings of the 2006 ACM SIGPLAN conference on Programming language design and implementation
Adaptive functional programming
ACM Transactions on Programming Languages and Systems (TOPLAS)
Information Processing Letters
Imperative self-adjusting computation
Proceedings of the 35th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Compiling self-adjusting programs with continuations
Proceedings of the 13th ACM SIGPLAN international conference on Functional programming
A cost semantics for self-adjusting computation
Proceedings of the 36th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages
CEAL: a C-based language for self-adjusting computation
Proceedings of the 2009 ACM SIGPLAN conference on Programming language design and implementation
An experimental analysis of self-adjusting computation
ACM Transactions on Programming Languages and Systems (TOPLAS)
Lazy functional incremental parsing
Proceedings of the 2nd ACM SIGPLAN symposium on Haskell
A Library for Self-Adjusting Computation
Electronic Notes in Theoretical Computer Science (ENTCS)
Verifying event-driven programs using ramified frame properties
Proceedings of the 5th ACM SIGPLAN workshop on Types in language design and implementation
A consistent semantics of self-adjusting computation
ESOP'07 Proceedings of the 16th European conference on Programming
Traceable data types for self-adjusting computation
PLDI '10 Proceedings of the 2010 ACM SIGPLAN conference on Programming language design and implementation
Self-adjusting computation with Delta ML
AFP'08 Proceedings of the 6th international conference on Advanced functional programming
Pull-ups, push-downs, and passing it around
IFL'09 Proceedings of the 21st international conference on Implementation and application of functional languages
Implicit self-adjusting computation for purely functional programs
Proceedings of the 16th ACM SIGPLAN international conference on Functional programming
Two for the price of one: a model for parallel and incremental computation
Proceedings of the 2011 ACM international conference on Object oriented programming systems languages and applications
Proceedings of the 2011 ACM international conference on Object oriented programming systems languages and applications
Type-directed automatic incrementalization
Proceedings of the 33rd ACM SIGPLAN conference on Programming Language Design and Implementation
Non-monotonic self-adjusting computation
ESOP'12 Proceedings of the 21st European conference on Programming Languages and Systems
An approach to verify live NCL applications
Proceedings of the 18th Brazilian symposium on Multimedia and the web
Pruning with improving sequences in lazy functional programs
Higher-Order and Symbolic Computation
| Hi-index | 0.00 |
This paper presents a monadic approach to incremental computation, suitable for purely functional languages such as Haskell. A program that uses incremental computation is able to perform an incremental amount of computation to accommodate for changes in input data. Recently, Acar, Blelloch and Harper presented a small Standard ML library that supports efficient, high-level incremental computations [1]. Here, we present a monadic variant of that library, written in Haskell extended with first-class references. By using monads, not only are we able to provide a purely functional interface to the library, the types also enforce "correct usage" without having to resort to any type-system extension. We also find optimization opportunities based on standard monadic combinators.This is an exercise in putting to work monad transformers with environments, references, and continuations.