Self-Adjusting Computation
Many interesting computational problems involve processing data that
changes over time, e.g., physical simulations often involve moving
objects, networks change as links are added or removed dynamically, a
robot may need to re-plan a path to its destination when it encounters
a previously unknown obstacle. We research computational models,
programming-languages, algorithms, and software systems for such
dynamic problems. Specifically, we develop the
self-adjusting-computation model where programs can respond
automatically and often efficiently to various modifications to their
data. The
Delta ML and CEAL languages realize self-adjusting computation by
extending Standard ML and C languages (respectively). Inspired by
these techniques, we investigate problems in computational geometry, statistical inference, provenance.
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