Applications of a strategy for designing divide-and-conquer algorithms
Science of Computer Programming
Randomized optimal algorithm for slope selection
Information Processing Letters
Randomized algorithms
Algorithms for the maximum subarray problem based on matrix multiplication
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Programming pearls: algorithm design techniques
Communications of the ACM
Efficient algorithms for k maximum sums
ISAAC'04 Proceedings of the 15th international conference on Algorithms and Computation
Improved algorithmms for the k maximum-sums problems
Theoretical Computer Science
Fast algorithms for finding disjoint subsequences with extremal densities
Pattern Recognition
A sub-cubic time algorithm for the k-maximum subarray problem
ISAAC'07 Proceedings of the 18th international conference on Algorithms and computation
Computing maximum-scoring segments in almost linear time
COCOON'06 Proceedings of the 12th annual international conference on Computing and Combinatorics
Efficient algorithms for the sum selection problem and k maximum sums problem
ISAAC'06 Proceedings of the 17th international conference on Algorithms and Computation
A linear time algorithm for the k maximal sums problem
MFCS'07 Proceedings of the 32nd international conference on Mathematical Foundations of Computer Science
| Hi-index | 0.00 |
Given a sequence of n real numbers A = a1, a2,..., an and a positive integer k, the Sum Selection Problem is to find the segment A( i,j)=ai, ai+1,..., aj such that the rank of the sum $s(i, j) = \sum_{t = i}^{j}{a_{t}}$ is k over all ${n(n-1)} \over {2}$ segments. We will give a randomized algorithm for this problem that runs in expected O(n log n) time. Applying this algorithm we can obtain an algorithm for the kMaximum Sums Problem, i.e., the problem of enumerating the k largest sum segments, that runs in expected O(n log n + k) time. The previously best known algorithm for the kMaximum Sums Problem runs in O(n log2n + k) time in the worst case.