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Computational Complexity and Simulation of Rare Events of Ising Spin Glasses
Martin Pelikan1, Jiri Ocenasek2, Simon Trebst2, Matthias Troyer2, and Fabien Alet2
1Dept. of Math. and Computer Science, 320 CCB, University of Missouri at St. Louis, 8001 Natural Bridge Rd., St. Louis, MO 63121
pelikan@cs.umsl.edu
2Computational Laboratory (CoLab), Swiss Federal Institute of Technology (ETH), CH-8092 Zürich, Switzerland
jirio@inf.ethz.ch
trebst@comp-phys.org
troyer@comp-phys.org
falet@comp-phys.org
Abstract. We discuss the computational complexity of random 2D Ising spin glasses, which represent an interesting class of constraint satisfaction problems for black box optimization. Two extremal cases are considered: (1) the ± J spin glass, and (2) the Gaussian spin glass. We also study a smooth transition between these two extremal cases. The computational complexity of all studied spin glass systems is found to be dominated by rare events of extremely hard spin glass samples. We show that complexity of all studied spin glass systems is closely related to Fréchet extremal value distribution. In a hybrid algorithm that combines the hierarchical Bayesian optimization algorithm (hBOA) with a deterministic bit-flip hill climber, the number of steps performed by both the global searcher (hBOA) and the local searcher follow Fréchet distributions. Nonetheless, unlike in methods based purely on local search, the parameters of these distributions confirm good scalability of hBOA with local search. We further argue that standard performance measures for optimization algorithms—such as the average number of evaluations until convergence—can be misleading. Finally, our results indicate that for highly multimodal constraint satisfaction problems, such as Ising spin glasses, recombination-based search can provide qualitatively better results than mutation-based search.
LNCS 3103, p. 36 ff.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2004