Dynamic Programming

Sniedovich, Moshe

doi:10.1201/ebk0824740993

Cited by 104 publications

(26 citation statements)

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“…SPED takes a new approach of calculating the distance of strings by representing the string matching process as a problem of computing the shortest path on a directed weighted acyclic graph. The Pulling algorithm (Sniedovich, 1992) designed for such graphs is applied to calculate the shortest path. Statistical dependencies in the strings are taken into account by implementing a compound substring matching operation in addition or in some cases instead of the insertion, deletion and substitution of character pairs.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…Classical algorithms are well known: single-source shortest path Dijkstra's algorithm (Dijkstra, 1959) for graphs with non-negative edges has complexity O(|V| 2 ); all-pairs shortest path Floyd algorithm (Floyd, 1962) for a weighted directed graph, O(|V| 3 ); single-source shortest path Bellman-Ford algorithm (Bellman, 1958;Ford and Fulkerson, 1962) can be used for a weighted graph allowing edge weights to be negative, O(|V| ⋅ |E|). Two more efficient algorithms which work for directed acyclic graphs and allow negative edge weights are the Reaching algorithm (Denardo, 2003) and the Pulling algorithm (Sniedovich, 1992) both working in O(n) time.…”

Section: Analysis Of Shortest Path Graph Algorithmsmentioning

confidence: 99%

“…The efficient Pulling algorithm (Sniedovich, 1992) is applied to calculate the shortest path. Statistical dependencies in the strings are taken into account by implementing a compound substring matching operation in addition or in some cases instead of the insertion, deletion and substitution of character pairs.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Detecting duplicate biological entities using Shortest Path Edit Distance

Rudniy

Song

Geller

2010

IJDMB

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Duplicate entity detection in biological data is an important research task. In this paper, we propose a novel and context-sensitive Shortest Path Edit Distance (SPED) extending and supplementing our previous work on Markov Random Field-based Edit Distance (MRFED). SPED transforms the edit distance computational problem to the calculation of the shortest path among two selected vertices of a graph. We produce several modifications of SPED by applying Levenshtein, arithmetic mean, histogram difference and TFIDF techniques to solve subtasks. We compare SPED performance to other well-known distance algorithms for biological entity matching. The experimental results show that SPED produces competitive outcomes.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Section: Analysis Of Shortest Path Graph Algorithmsmentioning

confidence: 99%

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Detecting duplicate biological entities using Shortest Path Edit Distance

Rudniy

Song

Geller

2010

IJDMB

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“…We use the dynamic-programming (DP) graph search algorithm [29] to search the local boundaries of a ROI within the band, such as the Dijkstra's algorithm or the A* algorithm [30]. To accelerate the searching of an optimal path with minimum cost, we employed an active list that sorts nodes by the total cost of starting nodes.…”

Section: Methodsmentioning

confidence: 99%

Iterative mesh transformation for 3D segmentation of livers with cancers in CT images

Harris

et al. 2015

Computerized Medical Imaging and Graphics

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Segmentation of diseased liver remains a challenging task in clinical applications due to the high inter-patient variability in liver shapes, sizes and pathologies caused by cancers or other liver diseases. In this paper, we present a multi-resolution mesh segmentation algorithm for 3D segmentation of livers, called iterative mesh transformation that deforms the mesh of a region-of-interest (ROI) in a progressive manner by iterations between mesh transformation and contour optimization. Mesh transformation deforms the 3D mesh based on the deformation transfer model that searches the optimal mesh based on the affine transformation subjected to a set of constraints of targeting vertices. Besides, contour optimization searches the optimal transversal contours of the ROI by applying the dynamic-programming algorithm to the intersection polylines of the 3D mesh on 2D transversal image planes. The initial constraint set for mesh transformation can be defined by a very small number of targeting vertices, namely landmarks, and progressively updated by adding the targeting vertices selected from the optimal transversal contours calculated in contour optimization. This iterative 3D mesh transformation constrained by 2D optimal transversal contours provides an efficient solution to a progressive approximation of the mesh of the targeting ROI. Based on this iterative mesh transformation algorithm, we developed a semi-automated scheme for segmentation of diseased livers with cancers using as little as five user-identified landmarks. The evaluation study demonstrates that this semiautomated liver segmentation scheme can achieve accurate and reliable segmentation results with significant reduction of interaction time and efforts when dealing with diseased liver cases.

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“…Therefore, we can solve these problems by using convex optimization techniques. Third, the design of joint bandwidth and power allocation to maximize the energy efficiency of the CR network has been formulated as the fractional programming (FP) problem [15]. To solve it, we transform it to the parametric formulation, which allows convex optimization technology to be applied to find the optimal bandwidth and power allocation strategy.…”

Section: Introductionmentioning

confidence: 99%

Bandwidth and power allocation for cooperative relay in cognitive radio networks

Tang

2012

EURASIP J. Adv. Signal Process.

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In this article, we consider a cognitive radio (CR) relay network where one source secondary user (SU) communicates with its corresponding destination SU with the help of relay SUs. Conventionally, equal bandwidth and/or power are allocated to each relay SU, which may not be efficient for the CR with limited bandwidth and power. Therefore, this article presents bandwidth and power allocation with amplify-and-forward (AF) or decode-and-forward (DF) relaying protocol to (1) maximize the sum network throughput; (2) minimize the total transmit power of the CR network with considering the fairness of power drain of relay SUs; (3) maximize the energy efficiency of the CR network. It is shown that DF relaying protocol can achieve better performance when the decoding rate constraint is not considered. In contrast, when considering the decoding rate constraint in DF relaying protocol, we propose the hybrid relaying protocol that combines AF and DF relaying protocols. We formulate the joint bandwidth and power allocation problem with hybrid relaying protocol to maximize the sum network throughput. A greedy algorithm is developed to solve the joint optimization problem, which has much less computational complexity. It is shown that the greedy algorithm has comparable performance to the exhaustive search algorithm. Finally, numerical results are provided to endorse our proposed algorithms.

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Dynamic Programming

Cited by 104 publications

References 0 publications

Detecting duplicate biological entities using Shortest Path Edit Distance

Detecting duplicate biological entities using Shortest Path Edit Distance

Iterative mesh transformation for 3D segmentation of livers with cancers in CT images

Bandwidth and power allocation for cooperative relay in cognitive radio networks

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