Mean-field constraints restore sparsity in Potts machines by replacing dense pairwise constraint couplings with dynamically updated single-node biases, achieving comparable partitioning quality with reduced density and accelerated FPGA performance.
Distributed Evolutionary Graph Partitioning
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abstract
We present a novel distributed evolutionary algorithm, KaFFPaE, to solve the Graph Partitioning Problem, which makes use of KaFFPa (Karlsruhe Fast Flow Partitioner). The use of our multilevel graph partitioner KaFFPa provides new effective crossover and mutation operators. By combining these with a scalable communication protocol we obtain a system that is able to improve the best known partitioning results for many inputs in a very short amount of time. For example, in Walshaw's well known benchmark tables we are able to improve or recompute 76% of entries for the tables with 1%, 3% and 5% imbalance.
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cond-mat.stat-mech 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Restoring Sparsity in Potts Machines via Mean-Field Constraints
Mean-field constraints restore sparsity in Potts machines by replacing dense pairwise constraint couplings with dynamically updated single-node biases, achieving comparable partitioning quality with reduced density and accelerated FPGA performance.