Bo Söderberg
Teaching staff
Optimization with Potts Neural Networks
Author
Editor
- M. S. Garrido
- R. Vilela Mendes
Summary, in 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.
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.
Department/s
- Computational Biology and Biological Physics - Has been reorganised
Publishing year
1992
Language
English
Pages
181-190
Publication/Series
Complexity in Physics and Technology
Document type
Conference paper
Publisher
World Scientific Publishing
Topic
- Bioinformatics (Computational Biology)
Status
Published