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12
Improved algorithms for the kmaximum subarray problem for small k
 In Proceedings of the 11th Annual International Conference on Computing and Combinatorics, volume 3595 of LNCS
, 2005
"... Abstract. The maximum subarray problem for a one or twodimensional array is to find the array portion that maiximizes the sum of array elements in it. The Kmaximum subarray problem is to find the K subarrays with largest sums. We improve the time complexity for the onedimensional case from O(min ..."
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Cited by 15 (5 self)
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Abstract. The maximum subarray problem for a one or twodimensional array is to find the array portion that maiximizes the sum of array elements in it. The Kmaximum subarray problem is to find the K subarrays with largest sums. We improve the time complexity for the onedimensional case from O(min{K + n log 2 n, n √ K}) for 0 ≤ K ≤ n(n − 1)/2 to O(n log K + K 2) for K ≤ n. The latter is better when K ≤ √ n log n. If we simply extend this result to the twodimensional case, we will have the complexity of O(n 3 log K + K 2 n 2).We improve this complexity to O(n 3) for K ≤ √ n. 1
A Linear Time Algorithm for the k Maximal Sums Problem
"... Abstract. Finding the subvector with the largest sum in a sequence of n numbers is known as the maximum sum problem. Finding the k subvectors with the largest sums is a natural extension of this, and is known as the k maximal sums problem. In this paper we design an optimal O(n+k) time algorithm f ..."
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Cited by 6 (2 self)
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Abstract. Finding the subvector with the largest sum in a sequence of n numbers is known as the maximum sum problem. Finding the k subvectors with the largest sums is a natural extension of this, and is known as the k maximal sums problem. In this paper we design an optimal O(n+k) time algorithm for the k maximal sums problem. We use this algorithm to obtain algorithms solving the twodimensional k maximal sums problem in O(m 2 ·n+k) time, where the input is an m ×n matrix with m ≤ n. We generalize this algorithm to solve the ddimensional problem in O(n 2d−1 +k) time. The space usage of all the algorithms can be reduced to O(n d−1 + k). This leads to the first algorithm for the k maximal sums problem in one dimension using O(n + k) time and O(k) space. 1
Computing maximumscoring segments in almost linear time
 In Proceedings of the 12th Annual International Computing and Combinatorics Conference, volume 4112 of LNCS
, 2006
"... Given a sequence, the problem studied in this paper is to find a set of k disjoint continuous subsequences such that the total sum of all elements in the set is maximized. This problem arises naturally in the analysis of DNA sequences. The previous best known algorithm requires Θ(n log n) time in th ..."
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Cited by 3 (1 self)
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Given a sequence, the problem studied in this paper is to find a set of k disjoint continuous subsequences such that the total sum of all elements in the set is maximized. This problem arises naturally in the analysis of DNA sequences. The previous best known algorithm requires Θ(n log n) time in the worst case. For a given sequence of length n, we present an almost lineartime algorithm for this problem. Our algorithm uses a disjointset data structure and requires O(nα(n, n)) time in the worst case, where α(n, n) is the inverse Ackermann function. 1
A note on ranking k maximum sums
"... In this paper, we design a fast algorithm for ranking the k maximum sum subsequences. Given a sequence of real numbers 〈x1, x2, · · · , xn 〉 and an integer parameter k, the problem is to compute k subsequences of consecutive elements with the sums of their elements being the largest, second large ..."
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Cited by 3 (0 self)
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In this paper, we design a fast algorithm for ranking the k maximum sum subsequences. Given a sequence of real numbers 〈x1, x2, · · · , xn 〉 and an integer parameter k, the problem is to compute k subsequences of consecutive elements with the sums of their elements being the largest, second largest,..., and the k th largest among all possible range sums. For any value of k, 1 ≤ k ≤ n(n + 1)/2, our algorithm takes O(n + k log n) time in the worst case to rank all such subsequences. Our algorithm is optimal for k ≤ n. 1
Algorithms for Finding the WeightConstrained k Longest Paths in a Tree and the LengthConstrained k MaximumSum Segments of a Sequence
, 2008
"... In this work, we obtain the following new results: – Given a tree T = (V, E) with a length function ℓ: E → R and a weight function w: E → R, a positive integer k, and an interval [L, U], the WeightConstrained k Longest Paths problem is to find the k longest paths among all paths in T with weights i ..."
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Cited by 2 (0 self)
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In this work, we obtain the following new results: – Given a tree T = (V, E) with a length function ℓ: E → R and a weight function w: E → R, a positive integer k, and an interval [L, U], the WeightConstrained k Longest Paths problem is to find the k longest paths among all paths in T with weights in the interval [L, U]. We show that the WeightConstrained k Longest Paths problem has a lower bound Ω(V log V + k) in the algebraic computation tree model and give an O(V log V + k)time algorithm for it. – Given a sequence A = (a1, a2,..., an) of numbers and an interval [L, U], we define the sum and length of a segment A[i, j] to be ai + ai+1 + · · · + aj and j − i + 1, respectively. The LengthConstrained k MaximumSum Segments problem is to find the k maximumsum segments among all segments of A with lengths in the interval [L, U]. We show that the LengthConstrained k MaximumSum Segments problem can be solved in O(n + k) time. ∗Corresponding
Ranking Cartesian Sums and Kmaximum subarrays
, 2006
"... We design a simple algorithm that ranks K largest in Cartesian sums X + Y in O(m + K log K) time. Based on this, Kmaximum subarrays can be computed in O(n + K log K) time (1D) and O(n 3 + K log K) time (2D) for input array of size n and n × n respectively. 1 ..."
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Cited by 1 (1 self)
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We design a simple algorithm that ranks K largest in Cartesian sums X + Y in O(m + K log K) time. Based on this, Kmaximum subarrays can be computed in O(n + K log K) time (1D) and O(n 3 + K log K) time (2D) for input array of size n and n × n respectively. 1
AVERAGE CASE ANALYSIS OF ALGORITHMS FOR THE MAXIMUM SUBARRAY PROBLEM
, 2007
"... Maximum Subarray Problem (MSP) is to find the consecutive array portion that maximizes the sum of array elements in it. The goal is to locate the most useful and informative array segment that associates two parameters involved in data in a 2D array. It’s an efficient data mining method which give ..."
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Maximum Subarray Problem (MSP) is to find the consecutive array portion that maximizes the sum of array elements in it. The goal is to locate the most useful and informative array segment that associates two parameters involved in data in a 2D array. It’s an efficient data mining method which gives us an accurate pattern or trend of data with respect to some associated parameters. Distance Matrix Multiplication (DMM) is at the core of MSP. Also DMM and MSP have the worstcase complexity of the same order. So if we improve the algorithm for DMM that would also trigger the improvement of MSP. The complexity of Conventional DMM is O(n 3). In the average case, All Pairs Shortest Path (APSP) Problem can be modified as a fast engine for DMM and can be solved in O(n 2 log n) expected time. Using this result, MSP can be solved in O(n 2 log 2 n) expected time. MSP