A refined algorithm for maximum independent set in degree-4 graphs

Mingyu Xiao () and Hiorshi Nagamochi ()
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Mingyu Xiao: University of Electronic Science and Technology of China
Hiorshi Nagamochi: Kyoto University

Journal of Combinatorial Optimization, 2017, vol. 34, issue 3, No 14, 830-873

Abstract: Abstract The maximum independent set problem is one of the most important problems in theoretical analysis on time and space complexities of exact algorithms. Theoretical improvement on upper bounds on time complexity to solve this problem in low-degree graphs can lead to an improvement on that to the problem in general graphs. In this paper, we derive an upper bound $$O^*(1.1376^n)$$ O ∗ ( 1 . 1376 n ) on the time complexity of a polynomial-space algorithm that solves the maximum independent set problem in an n-vertex graph with degree bounded by 4, improving all previous upper bounds on the time complexity of exact algorithms to this problem. Our algorithm is a branch-and-reduce algorithm and analyzed by using the measure-and-conquer method. To make an amortized analysis of the running time bound, we use an idea of “shift” to save some decrease of the measure from good branches to bad branches. Our algorithm first deals with small vertex cuts and vertices of degree $${\ge }5$$ ≥ 5 , which may be created in our algorithm even if the input graph has maximum degree 4, then eliminates cycles of length 3 and 4 containing degree-4 vertices, and finally branches on degree-4 vertices. We invoke an exact algorithm for this problem in graphs with maximum degree 3 directly when the graph has no vertices of degree $${\ge }4$$ ≥ 4 . Branching on degree-4 vertices on special local structures will be the bottleneck case, and we carefully design rules of choosing degree-4 vertices to branch on so that the resulting instances after branching decrease the measure effectively in the next step.

Keywords: Exact algorithms; Graph algorithms; Maximum independent set; Measure-and-conquer; Amortized analysis (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10878-017-0115-3

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