A decomposition method for global evaluation of shannon entropy and local estimations of algorithmic complexity

Hector Zenil*, Santiago Hernández-Orozco, Narsis A. Kiani, Fernando Soler-Toscano, Antonio Rueda-Toicen, Jesper Tegnér

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

40 Scopus citations


We investigate the properties of a Block Decomposition Method (BDM), which extends the power of a Coding Theorem Method (CTM) that approximates local estimations of algorithmic complexity based on Solomonoff-Levin's theory of algorithmic probability providing a closer connection to algorithmic complexity than previous attempts based on statistical regularities such as popular lossless compression schemes. The strategy behind BDM is to find small computer programs that produce the components of a larger, decomposed object. The set of short computer programs can then be artfully arranged in sequence so as to produce the original object. We show that the method provides efficient estimations of algorithmic complexity but that it performs like Shannon entropy when it loses accuracy. We estimate errors and study the behaviour of BDM for different boundary conditions, all of which are compared and assessed in detail. The measure may be adapted for use with more multi-dimensional objects than strings, objects such as arrays and tensors. To test the measure we demonstrate the power of CTM on low algorithmic-randomness objects that are assigned maximal entropy (e.g., π) but whose numerical approximations are closer to the theoretical low algorithmic-randomness expectation. We also test the measure on larger objects including dual, isomorphic and cospectral graphs for which we know that algorithmic randomness is low. We also release implementations of the methods in most major programming languages-Wolfram Language (Mathematica), Matlab, R, Perl, Python, Pascal, C++, and Haskell-and an online algorithmic complexity calculator.

Original languageEnglish (US)
Article number605
Issue number8
StatePublished - Aug 1 2018

Bibliographical note

Funding Information:
Funding: This research was funded by Swedish Research Council (Vetenskapsrådet) grant number [2015-05299].

Publisher Copyright:
© 2018 by the authors.


  • Algorithmic probability
  • Algorithmic randomness
  • Information content
  • Information theory
  • Kolmogorov-Chaitin complexity
  • Shannon entropy
  • Thue-Morse sequence
  • π

ASJC Scopus subject areas

  • Information Systems
  • Mathematical Physics
  • Physics and Astronomy (miscellaneous)
  • Electrical and Electronic Engineering


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