A Library for Self-Adjusting Computation

We present a Standard ML library for writing programs that automatically adjust to changes to their data. The library combines modifiable references and memoization to achieve efficient updates. We describe an implementation of the library and apply it to the problem of maintaining the convex hull of a dynamically changing set of points. Our experiments show that the overhead of the library is small, and that self-adjusting programs can adjust to small changes three-orders of magnitude faster than recomputing from scratch. The implementation relies on invariants that could be enforced by a modal type system. We show, using an existing language, abstract interfaces for modifiable references and for memoization that ensure the same safety properties without the use of modal types. The interface for memoization, however, does not scale well, suggesting a language-based approach to be preferable after all.