2005
DOI: 10.1007/11533719_63
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Improved Algorithms for the K-Maximum Subarray Problem for Small K

Abstract: Abstract. 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 O(min{K + n log 2 n, n √

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Cited by 16 publications
(22 citation statements)
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“…An improvement achieving O(n log K + K 2 ) time for small K is presented in the preliminary version of this paper [13]. This solution is better than the previous one when K ffiffi ffi n p log n and is O(n log K ) time when K ffiffiffiffiffiffiffiffiffiffiffiffiffi n log n p .…”
Section: Introductionmentioning
confidence: 87%
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“…An improvement achieving O(n log K + K 2 ) time for small K is presented in the preliminary version of this paper [13]. This solution is better than the previous one when K ffiffi ffi n p log n and is O(n log K ) time when K ffiffiffiffiffiffiffiffiffiffiffiffiffi n log n p .…”
Section: Introductionmentioning
confidence: 87%
“…We describe a simple solution that decreases the number of candidates from Kn to K 2 . Note that K 2 is considered to be smaller than Kn due to the assumption K n. This solution is introduced in the preliminary paper [13] and provides a starting point for the further improved algorithm in Section 4.…”
Section: Finding K Maximum Sums In O(kn) Timementioning
confidence: 99%
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“…, n] and a positive number k, consist in locate the k segments whose sum are the k largest among all possible sums. The k Maximum Sum Segments was first presented by Bae and Takaoka (5) and, after different solutions emerged (5,6,7,10,22,50), was optimally solved by Brodal and Jørgensen (15) in O(n + k)…”
Section: Related Workmentioning
confidence: 99%