Optimization with Potts Neural Networks
(1992) p.181-190- Abstract (Swedish)
- The Potts Neural Network approach to non-binary discrete optimization
problems is described. It applies to problems that can be described as
a set of elementary 'multiple choice' options. Instead of the conventional
binary (Ising) neurons, mean field Potts neurons, having several available
states, are used to describe the elementary degrees of freedom of such
problems. The dynamics consists of iterating the mean field equations
with annealing until convergence.
Due to its deterministic character, the method is quite fast. When
applied to problems of graph partition and scheduling types, it
produces very good solutions also for problems of considerable size.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3374dbdb-621f-478f-9180-3d8e5590d5f4
- author
- Söderberg, Bo LU
- organization
- publishing date
- 1992
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Complexity in Physics and Technology
- editor
- Garrido, M. S. and Vilela Mendes, R.
- pages
- 10 pages
- publisher
- World Scientific Publishing
- language
- English
- LU publication?
- yes
- id
- 3374dbdb-621f-478f-9180-3d8e5590d5f4
- date added to LUP
- 2019-05-13 19:58:12
- date last changed
- 2021-01-25 10:54:29
@inproceedings{3374dbdb-621f-478f-9180-3d8e5590d5f4, abstract = {{The Potts Neural Network approach to non-binary discrete optimization<br/>problems is described. It applies to problems that can be described as<br/>a set of elementary 'multiple choice' options. Instead of the conventional<br/>binary (Ising) neurons, mean field Potts neurons, having several available<br/>states, are used to describe the elementary degrees of freedom of such<br/>problems. The dynamics consists of iterating the mean field equations<br/>with annealing until convergence.<br/> Due to its deterministic character, the method is quite fast. When<br/>applied to problems of graph partition and scheduling types, it<br/>produces very good solutions also for problems of considerable size.}}, author = {{Söderberg, Bo}}, booktitle = {{Complexity in Physics and Technology}}, editor = {{Garrido, M. S. and Vilela Mendes, R.}}, language = {{eng}}, pages = {{181--190}}, publisher = {{World Scientific Publishing}}, title = {{Optimization with Potts Neural Networks}}, year = {{1992}}, }