Fast algorithms for finding nearest common ancestors
SIAM Journal on Computing
On finding lowest common ancestors: simplification and parallelization
SIAM Journal on Computing
Recursive star-tree parallel data structure
SIAM Journal on Computing
LATIN '00 Proceedings of the 4th Latin American Symposium on Theoretical Informatics
Linear-Time Longest-Common-Prefix Computation in Suffix Arrays and Its Applications
CPM '01 Proceedings of the 12th Annual Symposium on Combinatorial Pattern Matching
Opportunistic data structures with applications
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
A fast algorithm for the generalized k-keyword proximity problem given keyword offsets
Information Processing Letters
Lowest common ancestors in trees and directed acyclic graphs
Journal of Algorithms
Linear-time construction of suffix arrays
CPM'03 Proceedings of the 14th annual conference on Combinatorial pattern matching
Space efficient linear time construction of suffix arrays
CPM'03 Proceedings of the 14th annual conference on Combinatorial pattern matching
Simple linear work suffix array construction
ICALP'03 Proceedings of the 30th international conference on Automata, languages and programming
Theoretical and practical improvements on the RMQ-Problem, with applications to LCA and LCE
CPM'06 Proceedings of the 17th Annual conference on Combinatorial Pattern Matching
Construction of aho corasick automaton in linear time for integer alphabets
CPM'05 Proceedings of the 16th annual conference on Combinatorial Pattern Matching
Linear time algorithm for the generalised longest common repeat problem
SPIRE'05 Proceedings of the 12th international conference on String Processing and Information Retrieval
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The Range Minimum Query (RMQ) problem is to find the smallest element in an array for given range (a, b). We propose a simple and compact algorithm for this problem when the queries are sorted in ascending order. Then we show how to use this algorithm for the generalised longest common repeat problem [14]. Our algorithm is easy to understand and implement and requires much smaller memory.