Incremental assignment problem

Toroslu, İsmail Hakkı; íçoluk, Göktürk

doi:10.1016/j.ins.2006.05.004

Cited by 46 publications

(42 citation statements)

References 22 publications

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“…Similar to the work in [25], we can construct a weighted matrix for each pair of grams from two sequences, and apply the Hungarian algorithm [8,19]. Based on gram mapping distance, we show how a tighter lower bound on the edit distance between two sequences could be achieved.…”

Section: Definition 3 (Gram Mapping Distance)(gmd) Given Two Gram Mumentioning

confidence: 98%

Efficient and effective KNN sequence search with approximate n-grams

et al. 2013

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In this paper, we address the problem of finding k-nearest neighbors (KNN) in sequence databases using the edit distance. Unlike most existing works using short and exact ngram matchings together with a filter-and-refine framework for KNN sequence search, our new approach allows us to use longer but approximate n-gram matchings as a basis of KN-N candidates pruning. Based on this new idea, we devise a pipeline framework over a two-level index for searching KNN in the sequence database. By coupling this framework together with several efficient filtering strategies, i.e. the frequency queue and the well-known Combined Algorithm (CA), our proposal brings various enticing advantages over existing works, including 1) huge reduction on false positive candidates to avoid large overheads on candidate verifications; 2) progressive result update and early termination; and 3) good extensibility to parallel computation. We conduct extensive experiments on three real datasets to verify the superiority of the proposed framework.

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Section: Definition 3 (Gram Mapping Distance)(gmd) Given Two Gram Mumentioning

confidence: 98%

Efficient and effective KNN sequence search with approximate n-grams

et al. 2013

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“…Two relate directly to this paper: [15] provide an incremental Hungarian Method, which is an O(n 2 ) technique for inserting a pair of new vertices in the bipartite graph resulting from a previous assignment. An extension to this in [16] also permits deletions, which enables one to solve assignment problems with k utility changes in O(kn 2 ) time.…”

Section: Hungarian Algorithmmentioning

confidence: 99%

Assessing Optimal Assignment under Uncertainty: An Interval-based Algorithm

Liu¹,

Shell²

2010

Robotics: Science and Systems VI

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Abstract-We consider the problem of multi-robot taskallocation when robots have to deal with uncertain utility estimates. Typically an allocation is performed to maximize expected utility; we consider a means for measuring the robustness of a given optimal allocation when robots have some measure of the uncertainty (e.g., a probability distribution, or moments of such distributions). We introduce a new O(n 4 ) algorithm, the Interval Hungarian algorithm, that extends the classic KuhnMunkres Hungarian algorithm to compute the maximum interval of deviation (for each entry in the assignment matrix) which will retain the same optimal assignment. This provides an efficient measurement of the tolerance of the allocation to the uncertainties, for both a specific interval and a set of interrelated intervals. We conduct experiments both in simulation and with physical robots to validate the approach and to gain insight into the effect of location uncertainty on allocations for multi-robot multi-target navigation tasks.

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“…Conversely, [Toroslu andÜçoluk 2007] proposes an update module for the Hungarian algorithm. Given the current MCF solution, this method computes the new solution if a provider-customer pair (along with their incident edges) is appended to the current flow graph.…”

Section: Minimum Cost Flow On Bipartite Graphmentioning

confidence: 99%

“…[ Mills-Tettey et al 2007] extends the idea in [Toroslu andÜçoluk 2007] to handle multiple updates and address deletions (while still assuming one-to-one assignment). Specifically, letting |I| and |D| be the number of insertions and deletions, [Mills-Tettey et al 2007] applies the insertions/deletions onto the cost matrix and runs on it max{|I|, |D|} iterations of the Hungarian algorithm.…”

Section: Minimum Cost Flow On Bipartite Graphmentioning

confidence: 99%

Optimal matching between spatial datasets under capacity constraints

Mouratidis

Yiu

et al. 2010

ACM Trans. Database Syst.

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Consider a set of customers (e.g., WiFi receivers) and a set of service providers (e.g., wireless access points), where each provider has a capacity and the quality of service offered to its customers is anti-proportional to their distance. The capacity constrained assignment (CCA) is a matching between the two sets such that (i) each customer is assigned to at most one provider, (ii) every provider serves no more customers than its capacity, (iii) the maximum possible number of customers are served, and (iv) the sum of Euclidean distances within the assigned provider-customer pairs is minimized. Although max-flow algorithms are applicable to this problem, they require the complete distance-based bipartite graph between the customer and provider sets. For large spatial datasets, this graph is expensive to compute and it may be too large to fit in main memory. Motivated by this fact, we propose efficient algorithms for optimal assignment that employ novel edge-pruning strategies, based on the spatial properties of the problem. Additionally, we develop incremental techniques that maintain an optimal assignment (in the presence of updates) with a processing cost several times lower than CCA re-computation from scratch. Finally, we present approximate (i.e., suboptimal) CCA solutions that provide a tunable trade-off between result accuracy and computation cost, abiding by theoretical quality guarantees. A thorough experimental evaluation demonstrates the efficiency and practicality of the proposed techniques.

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Incremental assignment problem

Cited by 46 publications

References 22 publications

Efficient and effective KNN sequence search with approximate n-grams

Efficient and effective KNN sequence search with approximate n-grams

Assessing Optimal Assignment under Uncertainty: An Interval-based Algorithm

Optimal matching between spatial datasets under capacity constraints

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