Computational complexity of inferring phylogenies from dissimilarity matrices

  • Day W
  • 37


    Mendeley users who have this article in their library.
  • 122


    Citations of this article.


Molecular biologists strive to infer evolutionary relationships from quantitative macromolecular comparisons obtained by immunological, DNA hybridization, electrophoretic or amino acid sequencing techniques. The problem is to find unrooted phylogenies that best approximate a given dissimilarity matrix according to a goodness-of-fit measure, for example the least-squares-fit criterion or Farris's f statistic. Computational costs of known algorithms guaranteeing optimal solutions to these problems increase exponentially with problem size; practical computational considerations limit the algorithms to analyzing small problems. It is established here that problems of phylogenetic inference based on the least-squares-fit criterion and the f statistic are NP-complete and thus are so difficult computationally that efficient optimal algorithms are unlikely to exist for them.

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document


  • William H.E. Day

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free