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Updated: Aug 8, 2026
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A Practical Guide to Phylogenetics for Nonexperts
Published on: February 5, 2014
Phylogenetic Inference under the Balanced Minimum Evolution Criterion via Semidefinite Programming
1School of Computing, University of Connecticut, 371 Fairfield Way, Storrs, 06269, CT, USA.
Bioinformatics (Oxford, England)
|August 6, 2026
Summary
This study introduces Semidefinite Programming (SDP) for phylogenetic inference, addressing the Balanced Minimum Evolution problem. The novel method combines SDP relaxations with rounding schemes for accurate tree topology reconstruction.
Area of Science:
- Computational Biology
- Phylogenetics
- Optimization
Background:
- Semidefinite Programming (SDP) is a powerful optimization framework with broad applications.
- SDP is largely underutilized in computational biology and phylogenetics.
- This study explores SDP's potential for phylogenetic inference.
Purpose of the Study:
- To investigate the application of Semidefinite Programming (SDP) to phylogenetics.
- To develop and evaluate an SDP-based method for phylogenetic inference.
- To address the Balanced Minimum Evolution (BME) problem using SDP.
Main Methods:
- Designed SDP relaxations for phylogenetic inference.
- Developed an algorithm combining SDP relaxation with a rounding scheme.
- Iteratively converted relaxed solutions into valid tree topologies.
Main Results:
- Demonstrated the use of SDP relaxations for phylogenetic inference.
- The proposed method enables accurate phylogenetic reconstruction.
- Experiments on simulated and empirical datasets validated the approach.
Conclusions:
- SDP offers a promising approach for phylogenetic inference.
- The developed algorithm effectively reconstructs phylogenetic trees.
- This work highlights the potential of SDP in computational biology.
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