Numerical Optimization Techniques in Maximum Likelihood Tree Inference

  • Stéphane Guindon
  • Olivier GascuelEmail author
Part of the Computational Biology book series (COBO, volume 29)


In this chapter, we present recent computational and algorithmic advances for improving the inference of phylogenetic trees from the analysis of homologous genetic sequences under the maximum likelihood criterion. In particular, we detail how the use of matrix algebra at the core of Felsenstein’s pruning algorithm, combined with the architecture of modern day computer processors, leads to efficient techniques for optimizing edge lengths. We also discuss some properties of the likelihood function when considering the optimization of the parameters of mixture models that are used to describe the variation of rates-across sites .


Maximum likelihood Markov processes Optimization Mixture models 



We would like to thank Alexandros Stamatakis for helpful suggestions on how to improve this chapter and Tandy Warnow for inviting us to celebrate Bernard Moret’s contributions to the field of computational evolution.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Laboratoire d’Informatique de Robotique et de Microélectronique de MontpellierCNRS and Université Montpellier (UMR 5506)MontpellierFrance
  2. 2.Unité Bioinformatique EvolutiveC3BI Institut Pasteur and CNRS (USR 3756)ParisFrance

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