Algorithm 415: Algorithm for the assignment problem (rectangular matrices)

Bourgeois, F.; Lassalle, J. C.

doi:10.1145/362919.362948

Cited by 10 publications

(3 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the matrix WM(i, j) may not be square. Even though there are extensions of the Hungarian algorithm for rectangular matrices [7], the approach chosen here was to be padded the non-square matrices with dummy costs. The addition of dummy costs serves to penalize a query shape that has additional parts, especially if these additional parts are visually salient.…”

Section: Matching Parts and Shapesmentioning

confidence: 99%

Strategies for shape matching using skeletons

Goh¹

2008

Computer Vision and Image Understanding

View full text Add to dashboard Cite

Section: Matching Parts and Shapesmentioning

confidence: 99%

Strategies for shape matching using skeletons

Goh¹

2008

Computer Vision and Image Understanding

View full text Add to dashboard Cite

“…The constraint in is due to the facts that each channel can be assigned to at most one group, and

G ⩽ N

. The problem in – is a standard assignment problem, and the optimal π * can be efficiently solved using the modified Munkres algorithm with a polynomial time of

scriptO (G^{2} N)

, which is much smaller than the complexity of the exhaustive search method

scriptO (N MathClass-punc! MathClass-bin∕ (N MathClass-bin- G) MathClass-punc!)

. Furthermore, it is easy to see that

D^{MathClass-rel'} (bold-italicλ MathClass-punc, μ) MathClass-rel= {bold 1}_{G}^{T} (bold-italicC MathClass-bin⊙ bold-italicχ (π^{MathClass-bin*})) {bold 1}_{N}

.…”

Section: Joint Channel and Power Allocation For Outage Minimisation Imentioning

confidence: 99%

“…, / if channel n is assigned to group g. The constraint in (17) is due to the fact that each group can be assigned by one and only one channel. The constraint in (18) is due to the facts that each channel can be assigned to at most one group, and G 6 N. The problem in (16)-(18) is a standard assignment problem, and the optimal can be efficiently solved using the modified Munkres algorithm ‡ [25,26] with a polynomial time of O.G 2 N/, which is much smaller than the complexity of the exhaustive search method O.NŠ=.N G/Š/. Furthermore, it is easy to see that D 0 .…”

Section: Group Outage Probability Minimisationmentioning

confidence: 99%

Resource allocation for outage probability minimisation in cognitive radio multicast networks

2014

Trans Emerging Tel Tech

View full text Add to dashboard Cite

In this paper, we consider a downlink secondary cognitive radio multicast network that consists of a cognitive base station and a set of secondary multicast groups sharing spectrum with a primary network. To protect the transmissions of the primary network, an interference power constraint is applied to restrict the transmit power of the cognitive base station. The problem of joint channel and power allocation for the cognitive radio multicast network is studied. The objective is to minimise the weighted aggregate outage probability for a given target rate for the cognitive radio multicast network. Specifically, two types of outage probabilities, namely, group outage probability and individual outage probability, are considered. For each type of outage probability, the optimal joint channel and power allocation algorithm is proposed based on Lagrange dual optimisation. Simulation results are illustrated to verify the effectiveness of the proposed algorithms. It is shown that the proposed algorithms significantly improve the performance of the cognitive radio multicast network in terms of outage probability. Copyright © 2014 John Wiley & Sons, Ltd.

show abstract

Parallel approaches to the solution of the assignment problem

Davis

Carpenter²,

Glover

et al. 1992

Concurrency: Pract. Exper.

View full text Add to dashboard Cite

This paper discusses efforts to develop parallel algorithms that can be used to solve large-scale assignment problems typical of military battle management systems. Attempts to parallellze the classical Hungarian method are explored. Drawing on experimental data, a regression model is derived that relates the Hungarian method's run time to a cubk function of the number of parallel processors used in the solution. Four parallel heuristics for solving the assignment problem are developed and analysed. Two of them perform well in a parallel environment. The first, based on Vagel's approximation, can be used to identify a feasible, near-optlmal assignment. The second algorlthm partitions the assignment problem into independent subproblems across the parallel array. The resulting solution, although infeasible in the strkt deenltion of the assignment problem, is seen to be reasonably good and can be obtained very rapidly. Either of these two heuristics are of potentlal use in the stringent computing environment of a real-time resource management system.

show abstract

Algorithm 415: Algorithm for the assignment problem (rectangular matrices)

Abstract: This algorithm is a companion to [3] where the theoretical background is described.

Cited by 10 publications

References 3 publications

Strategies for shape matching using skeletons

Strategies for shape matching using skeletons

Resource allocation for outage probability minimisation in cognitive radio multicast networks

Parallel approaches to the solution of the assignment problem

Contact Info

Product

Resources

About