Approximation and Intractability Results for the Maximum Cut Problem and Its Variants
IEEE Transactions on Computers
MAX-CUT has a randomized approximation scheme in dense graphs
Random Structures & Algorithms
Fast Approximation Algorithms on Maxcut, k-Coloring, and k-Color Ordering for VLSI Applications
IEEE Transactions on Computers
P-Complete Approximation Problems
Journal of the ACM (JACM)
Gadgets, Approximation, and Linear Programming
SIAM Journal on Computing
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
Local Search in Combinatorial Optimization
Local Search in Combinatorial Optimization
A randomized approximation scheme for metric MAX-CUT
Journal of Computer and System Sciences
Rank-Two Relaxation Heuristics for MAX-CUT and Other Binary Quadratic Programs
SIAM Journal on Optimization
Improved approximation of max-cut on graphs of bounded degree
Journal of Algorithms
New Approximation Results on Graph Matching and related Problems
WG '94 Proceedings of the 20th International Workshop on Graph-Theoretic Concepts in Computer Science
Mining newsgroups using networks arising from social behavior
WWW '03 Proceedings of the 12th international conference on World Wide Web
Lagrangian Smoothing Heuristics for Max-Cut
Journal of Heuristics
Geometry of Cuts and Metrics
Competitive simulated annealing and Tabu Search algorithms for the max-cut problem
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Using landscape measures for the online tuning of heterogeneous distributed gas
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
A Hopfield neural network applied to the fuzzy maximum cut problem under credibility measure
Information Sciences: an International Journal
Path Relinking Scheme for the Max-Cut Problem within Global Equilibrium Search
International Journal of Swarm Intelligence Research
Breakout Local Search for the Max-Cutproblem
Engineering Applications of Artificial Intelligence
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Given a graph with non-negative edge weights, the MAX CUT problem is to partition the set of vertices into two subsets so that the sum of the weights of edges with endpoints in different subsets is maximised. This classical NP-hard problem finds applications in VLSI design, statistical physics, and classification among other fields. This paper compares the performance of several greedy construction heuristics for MAX-CUT problem. In particular, a new 'worst-out' approach is studied and the proposed edge contraction heuristic is shown to have an approximation ratio of at least 1/3. The results of experimental comparison of the worst-out approach, the well-known best-in algorithm, and modifications for both are also included.