Sparse Dynamic Programming II: Convex and Concave Cost Functions
We consider dynamic programming solutions to a number of different recurrences for sequence comparison and for RNA secondary structure prediction. These recurrences are defined over a number of points that is quadratic in the input size; however only a sparse set matters for the result. We give efficient algorithms for these problems, when the weight functions used in the recurrences are taken to be linear. Our algorithms reduce the best known bounds by a factor almost linear in the density of the problems: when the problems are sparse this results in a substantial speed-up.
AC:P:12181
Academic Commons
Eppstein, David
Author
Galil, Zvi
Author
Giancarlo, Raffaele
Author
Italiano, Giuseppe F.
Author
Columbia University. Computer Science
Originator
Contributor
Reports
English
Computer science
Columbia University Computer Science Technical Reports
CUCS-472-89
text
Department of Computer Science, Columbia University
1989
New York
eng
2012-01-11T20:13:18Z
2018-02-16T23:52:41Z
10.7916/D8W09F2S