Slowing down sorting networks to obtain faster sorting algorithms
Journal of the ACM (JACM)
Applications of a strategy for designing divide-and-conquer algorithms
Science of Computer Programming
Sorting in c log n parallel steps
Combinatorica
An optimal-time algorithm for slope selection
SIAM Journal on Computing
Data mining using two-dimensional optimized association rules: scheme, algorithms, and visualization
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Algorithms for the maximum subarray problem based on matrix multiplication
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Applying Parallel Computation Algorithms in the Design of Serial Algorithms
Journal of the ACM (JACM)
Programming pearls: perspective on performance
Communications of the ACM
Programming pearls: algorithm design techniques
Communications of the ACM
Efficient Algorithms for k Maximum Sums
Algorithmica
Improved Algorithms for the K-Maximum Subarray Problem
The Computer Journal
Improved algorithmms for the k maximum-sums problems
Theoretical Computer Science
Randomized algorithm for the sum selection problem
Theoretical Computer Science
Hi-index | 5.23 |
Given a sequence of n real numbers A=a"1,a"2,...,a"n and a positive integer k, the Sum Selection Problem is to find the segment A(i^*,j^*)=a"i"^"*,a"i"^"*"+"1,...,a"j"^"* such that the rank of the sum s(i^*,j^*)=@?"t"="i"^"*^j^^^*a"t is k over all n(n-1)2 segments. We present a deterministic algorithm for this problem that runs in O(nlogn) time. The previously best known result for this problem is a randomized algorithm that runs in expected O(nlogn) time. Applying this algorithm we can obtain a deterministic algorithm for the k Maximum Sums Problem, i.e., the problem of enumerating the k largest sum segments, that runs in O(nlogn+k) time. The previously best known randomized and deterministic algorithms for the k Maximum Sums Problem run respectively in expected O(nlogn+k) time and in worst case O(nlog^2n+k) time.