Show simple item record

dc.contributor.authorZenil, Hector
dc.contributor.authorKiani, Narsis A.
dc.contributor.authorTegner, Jesper
dc.date.accessioned2018-03-15T11:35:54Z
dc.date.available2018-03-15T11:35:54Z
dc.date.issued2018-02-24
dc.identifier.urihttp://hdl.handle.net/10754/627341
dc.description.abstractWe introduce a definition of algorithmic symmetry able to capture essential aspects of geometric symmetry. We review, study and apply a method for approximating the algorithmic complexity (also known as Kolmogorov-Chaitin complexity) of graphs and networks based on the concept of Algorithmic Probability (AP). AP is a concept (and method) capable of recursively enumeration all properties of computable (causal) nature beyond statistical regularities. We explore the connections of algorithmic complexity---both theoretical and numerical---with geometric properties mainly symmetry and topology from an (algorithmic) information-theoretic perspective. We show that approximations to algorithmic complexity by lossless compression and an Algorithmic Probability-based method can characterize properties of polyominoes, polytopes, regular and quasi-regular polyhedra as well as polyhedral networks, thereby demonstrating its profiling capabilities.
dc.publisherarXiv
dc.relation.urlhttp://arxiv.org/abs/1803.02186v1
dc.relation.urlhttp://arxiv.org/pdf/1803.02186v1
dc.rightsArchived with thanks to arXiv
dc.titleSymmetry and Algorithmic Complexity of Polyominoes and Polyhedral Graphs
dc.typePreprint
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Division
dc.contributor.departmentBioscience Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.eprint.versionPre-print
dc.contributor.institutionAlgorithmic Nature Group, LABORES for the Natural and Digital Sciences, Paris, France
dc.contributor.institutionScience for Life Laboratory, SciLifeLab, Stockholm, Sweden
dc.contributor.institutionUnit of Computational Medicine, Department of Medicine, Karolinska Institute, Stockholm, Sweden
dc.contributor.institutionAlgorithmic Dynamics Lab, Centre for Molecular Medicine, Karolinska Institute, Stockholm, Sweden
dc.identifier.arxivid1803.02186
kaust.personTegner, Jesper
dc.versionv1
refterms.dateFOA2018-06-14T05:50:44Z


Files in this item

Thumbnail
Name:
1803.02186v1.pdf
Size:
751.2Kb
Format:
PDF
Description:
Preprint

This item appears in the following Collection(s)

Show simple item record