A method to construct 1-rotational factorizations of complete graphs and solutions to the oberwolfach problem

Daniel McGinnis; Eirini Poimenidou

doi:10.1002/jcd.21643

On factorisations of complete graphs into circulant graphs and the Oberwolfach problem

Ars Mathematica Contemporanea ◽

10.26493/1855-3974.770.150 ◽

2015 ◽

Vol 11 (1) ◽

pp. 157-173 ◽

Cited By ~ 3

Author(s):

Brian Alspach ◽

Darryn Bryant ◽

Daniel Horsley ◽

Barbara Maenhaut ◽

Victor Scharaschkin

Keyword(s):

Complete Graphs ◽

Circulant Graphs ◽

Oberwolfach Problem

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Roulette Wheel Selection based Heuristic Algorithm for the Orienteering Problem

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY ◽

10.24297/ijct.v13i1.2933 ◽

2014 ◽

Vol 13 (1) ◽

pp. 4127-4145

Author(s):

Madhushi Verma ◽

Mukul Gupta ◽

Bijeeta Pal ◽

Prof. K. K. Shukla

Keyword(s):

Triangle Inequality ◽

Time Budget ◽

Control Point ◽

Hamiltonian Path ◽

Real Life ◽

Tourism Industry ◽

Orienteering Problem ◽

Complete Graphs ◽

Roulette Wheel Selection ◽

Roulette Wheel

Orienteering problem (OP) is an NP-Hard graph problem. The nodes of the graph are associated with scores or rewards and the edges with time delays. The goal is to obtain a Hamiltonian path connecting the two necessary check points, i.e. the source and the target along with a set of control points such that the total collected score is maximized within a specified time limit. OP finds application in several fields like logistics, transportation networks, tourism industry, etc. Most of the existing algorithms for OP can only be applied on complete graphs that satisfy the triangle inequality. Real-life scenario does not guarantee that there exists a direct link between all control point pairs or the triangle inequality is satisfied. To provide a more practical solution, we propose a stochastic greedy algorithm (RWS_OP) that uses the roulette wheel selectionmethod, does not require that the triangle inequality condition is satisfied and is capable of handling both complete as well as incomplete graphs. Based on several experiments on standard benchmark data we show that RWS_OP is faster, more efficient in terms of time budget utilization and achieves a better performance in terms of the total collected score ascompared to a recently reported algorithm for incomplete graphs.

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The quadrangular genus of complete graphs

Problems of theoretical cybernetics ◽

10.29003/m69.ptk18.2017/11-13 ◽

2017 ◽

Author(s):

Serge Lawrencenko

Keyword(s):

Complete Graphs

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Improving the Smoothed Complexity of FLIP for Max Cut Problems

ACM Transactions on Algorithms ◽

10.1145/3454125 ◽

2021 ◽

Vol 17 (3) ◽

pp. 1-38

Author(s):

Ali Bibak ◽

Charles Carlson ◽

Karthekeyan Chandrasekaran

Keyword(s):

General Framework ◽

Edge Weight ◽

Complete Graphs ◽

Worst Case ◽

Weight Density ◽

Complete Problems ◽

Substantial Progress ◽

Run Time ◽

Optimum Solution ◽

Arbitrary Graphs

Finding locally optimal solutions for MAX-CUT and MAX- k -CUT are well-known PLS-complete problems. An instinctive approach to finding such a locally optimum solution is the FLIP method. Even though FLIP requires exponential time in worst-case instances, it tends to terminate quickly in practical instances. To explain this discrepancy, the run-time of FLIP has been studied in the smoothed complexity framework. Etscheid and Röglin (ACM Transactions on Algorithms, 2017) showed that the smoothed complexity of FLIP for max-cut in arbitrary graphs is quasi-polynomial. Angel, Bubeck, Peres, and Wei (STOC, 2017) showed that the smoothed complexity of FLIP for max-cut in complete graphs is ( O Φ 5 n 15.1 ), where Φ is an upper bound on the random edge-weight density and Φ is the number of vertices in the input graph. While Angel, Bubeck, Peres, and Wei’s result showed the first polynomial smoothed complexity, they also conjectured that their run-time bound is far from optimal. In this work, we make substantial progress toward improving the run-time bound. We prove that the smoothed complexity of FLIP for max-cut in complete graphs is O (Φ n 7.83 ). Our results are based on a carefully chosen matrix whose rank captures the run-time of the method along with improved rank bounds for this matrix and an improved union bound based on this matrix. In addition, our techniques provide a general framework for analyzing FLIP in the smoothed framework. We illustrate this general framework by showing that the smoothed complexity of FLIP for MAX-3-CUT in complete graphs is polynomial and for MAX - k - CUT in arbitrary graphs is quasi-polynomial. We believe that our techniques should also be of interest toward showing smoothed polynomial complexity of FLIP for MAX - k - CUT in complete graphs for larger constants k .

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