Survey of Algorithms on Maximum Clique Problem

Singh, Krishna Kumar; Pandey, Ajeet Kumar

doi:10.17148/iarjset.2015.2203

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…It is crucial to find a good lower bound on early in the B&B algorithm to prevent the growth of the B&B tree and therefore solve the stable set problem more efficiently. In Singh and Pandey ( 2015 ) and Wu and Hao ( 2015 ), for example, one can find references to some heuristics to find a large stable set. In our implementation we use several different heuristics.…”

Section: Branch and Bound For The Stable Set Problemmentioning

confidence: 99%

An SDP-based approach for computing the stability number of a graph

2022

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Finding the stability number of a graph, i.e., the maximum number of vertices of which no two are adjacent, is a well known NP-hard combinatorial optimization problem. Since this problem has several applications in real life, there is need to find efficient algorithms to solve this problem. Recently, Gaar and Rendl enhanced semidefinite programming approaches to tighten the upper bound given by the Lovász theta function. This is done by carefully selecting some so-called exact subgraph constraints (ESC) and adding them to the semidefinite program of computing the Lovász theta function. First, we provide two new relaxations that allow to compute the bounds faster without substantial loss of the quality of the bounds. One of these two relaxations is based on including violated facets of the polytope representing the ESCs, the other one adds separating hyperplanes for that polytope. Furthermore, we implement a branch and bound (B&B) algorithm using these tightened relaxations in our bounding routine. We compare the efficiency of our B&B algorithm using the different upper bounds. It turns out that already the bounds of Gaar and Rendl drastically reduce the number of nodes to be explored in the B&B tree as compared to the Lovász theta bound. However, this comes with a high computational cost. Our new relaxations improve the run time of the overall B&B algorithm, while keeping the number of nodes in the B&B tree small.

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Section: Branch and Bound For The Stable Set Problemmentioning

confidence: 99%

An SDP-based approach for computing the stability number of a graph

2022

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“…It is crucial to find a good lower bound on α(G) early in the B&B algorithm to prevent the growth of the B&B tree and therefore solve the stable set problem more efficiently. In [24] and [25], for example, one can find references to some heuristics to find a large stable set.…”

Section: Heuristics To Find Large Stable Setsmentioning

confidence: 99%

An SDP-Based Approach for Computing the Stability Number of a Graph

Gaar,

Siebenhofer,

Wiegele

2021

Preprint

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Finding the stability number of a graph, i.e., the maximum number of vertices of which no two are adjacent, is a well known NP-hard combinatorial optimization problem. Since this problem has several applications in real life, there is need to find efficient algorithms to solve this problem. Recently, Gaar and Rendl enhanced semidefinite programming approaches to tighten the upper bound given by the Lovász theta function. This is done by carefully selecting some so-called exact subgraph constraints (ESC) and adding them to the semidefinite program of computing the Lovász theta function.First, we provide two new relaxations that allow to compute the bounds faster without substantial loss of the quality of the bounds. One of these two relaxations is based on including violated facets of the polytope representing the ESCs, the other one adds separating hyperplanes for that polytope.Furthermore, we implement a branch and bound (B&B) algorithm using these tightened relaxations in our bounding routine. We compare the efficiency of our B&B algorithm using the different upper bounds. It turns out that already the bounds of Gaar and Rendl drastically reduce the number of nodes to be explored in the B&B tree as compared to the Lovász theta bound. However, this comes with a high computational cost. Our new relaxations improve the run time of the overall B&B algorithm, while keeping the number of nodes in the B&B tree small.

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