Paper Info
Title
Learning to Prune: Speeding up Repeated Computations
Abstract
It is common to encounter situations where one must solve a sequence of similar computational problems. Running a standard algorithm with worst-case runtime guarantees on each instance will fail to take advantage of valuable structure shared across the problem instances. For example, when a commuter drives from work to home, there are typically only a handful of routes that will ever be the shortest path. A naive algorithm that does not exploit this common structure may spend most of its time checking roads that will never be in the shortest path. More generally, we can often ignore large swaths of the search space that will likely never contain an optimal solution. present an algorithm that learns to maximally prune the search space on repeated computations, thereby reducing runtime while provably outputting the correct solution each period with high probability. Our algorithm employs a simple explore-exploit technique resembling those used in online algorithms, though our setting is quite different. We prove that, with respect to our model of pruning search spaces, our approach is optimal up to constant factors. Finally, we illustrate the applicability of our model and algorithm to three classic problems: shortest-path routing, string search, and linear programming. We present experiments confirming that our simple algorithm is effective at significantly reducing the runtime of solving repeated computations.
Year
Venue
Field
2019
COLT
String searching algorithm,Online algorithm,Standard algorithms,Computational problem,Mathematical optimization,Shortest path problem,Theoretical computer science,Linear programming,SIMPLE algorithm,Mathematics,Computation
DocType
Volume
Citations 
Journal
abs/1904.11875
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Daniel Alabi1132.23
Adam Tauman Kalai21620115.10
Katrina Ligett392366.19
Cameron Musco425825.06
Christos Tzamos513325.48
Ellen Vitercik6237.23