@inproceedings {760,
	title = {Hierarchical annealing for scientific models},
	booktitle = {37th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
	year = {2004},
	address = {Montreal},
	abstract = {<p>The computational complexity of simulated annealing makes it an impractical tool in many applications, particularly for complex, non-local models on very large 2D and 3D domains as desired in many scientific contexts. In particular, it is very difficult to produce large scale structure from a fine, pixellated lattice. Thus a hierarchical approach is intuitively attractive. However, existing approaches are few and limited. Motivated by a current problem in porous media, we develop a hierarchical approach to complex model sampling. In experiments, this approach results in 1-2 orders of magnitude computational gain, and significant gains in convergence as well.</p>
},
	keywords = {2D domains, 3D domains, complex model sampling, computational complexity, data visualisation, hierarchical annealing, hierarchical sampling, image resolution, image sampling, pixellated lattice, porous materials, porous media, sampled images, scientific models, simulated annealing},
	doi = {http://dx.doi.org/10.1109/ICASSP.2004.1326474},
	author = {S K. Alexander and P Fieguth and E Vrscay}
}
