Graph partitioning through a multi-objective evolutionary algorithm: a preliminary study
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Partitioning graphs into balanced components
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
Parallelizing SystemC Kernel for Fast Hardware Simulation on SMP Machines
PADS '09 Proceedings of the 2009 ACM/IEEE/SCS 23rd Workshop on Principles of Advanced and Distributed Simulation
Simple cuts are fast and good: optimum right-angled cuts in solid grids
COCOA'10 Proceedings of the 4th international conference on Combinatorial optimization and applications - Volume Part I
Graph partitioning by multi-objective real-valued metaheuristics: A comparative study
Applied Soft Computing
Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete Algorithms
Streaming graph partitioning for large distributed graphs
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
On the solution of a graph partitioning problem under capacity constraints
ISCO'12 Proceedings of the Second international conference on Combinatorial Optimization
Fast balanced partitioning is hard even on grids and trees
MFCS'12 Proceedings of the 37th international conference on Mathematical Foundations of Computer Science
ACM Transactions on Embedded Computing Systems (TECS) - Special issue on application-specific processors
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We consider the problem of partitioning a graph into k components of roughly equal size while minimizing the capacity of the edges between different components of the cut. In particular we require that for a parameter ν ≥ 1, no component contains more than ν · n/k of the graph vertices.For k = 2 and ν = 1 this problem is equivalent to the well-known Minimum Bisection problem for which an approximation algorithm with a polylogarithmic approximation guarantee has been presented in [FK]. For arbitrary k and ν ≥ 2 a bicriteria approximation ratio of O(log n) was obtained by Even et al. [ENRS1] using the spreading metrics technique.We present a bicriteria approximation algorithm that for any constant ν 1 runs in polynomial time and guarantees an approximation ratio of O(log1.5n) (for a precise statement of the main result see Theorem 6). For ν = 1 and k ≥ 3 we show that no polynomial time approximation algorithm can guarantee a finite approximation ratio unless P = NP.