Sung Eun Bae

Algorithms for the problem of K maximum sums and a VLSI algorithm for the K maximum subarrays problem

Given an array of positive and negative values, we consider the problem of K maximum sums. When an overlapping property needs to be observed, previous algorithms for the maximum sum are not directly applicable. We designed an O(K * n) algorithm for the K maximum subsequences problem. This was then modified to solve the K maximum subarrays problem in O(K * n/sup 3/) time. Finally, we present a VLSI K maximum subarrays algorithm with O(K * n) steps and a circuit size of O(n/sup 2/), which is cost-optimal in parallelisation of the sequential algorithm.

Improved Algorithms for the K-Maximum Subarray Problem

The maximum subarray problem is to find the contiguous array elements having the largest possible sum. We extend this problem to find K maximum subarrays. For general K maximum subarrays where overlapping is allowed, Bengtsson and Chen presented

Algorithm for K disjoint maximum subarrays

O\left(\mathit{min}\right\{K+n{\hbox{ log }}^{2}n,n\sqrt{K}\left\}\right)

A sub-cubic time algorithm for the k-maximum subarray problem

time algorithm for one-dimensional case, which finds unsorted subarrays. Our algorithm finds K maximum subarrays in sorted order with improved complexity of O ((n + K) log K). For the two-dimensional case, we introduce two techniques that establish O(n3) and subcubic time.

Efficient Algorithms for the Maximum Sum Problems

The maximum subarray problem for a one- or two-dimensional array is to find the array portion that maiximizes the sum of array elements in it. The K-maximum subarray problem is to find the K subarrays with largest sums. We improve the time complexity for the one-dimensional case from

Minimum Weight Polygon Triangulation Problem in Sub-Cubic Time Bound

O(min\{K+n\log^2 n, n\sqrt{K}\})

An efficient parallel algorithm for the maximum convex sum problem

for 0 ≤ K ≤ n(n–1)/2 to O(nlog K + K2) for K ≤ n. The latter is better when

ALGORITHMS FOR K-DISJOINT MAXIMUM SUBARRAYS

K \le \sqrt n\log n