Phylogenetic Inference under the Balanced Minimum Evolution Criterion via Semidefinite Programming
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ID: 324226
2026
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Abstract
MOTIVATION: In this study, we investigate the application of Semidefinite Programming (SDP) to phylogenetics. SDP is a powerful optimization framework that seeks to optimize a linear objective function over the cone of positive semidefinite matrices. As a convex optimization problem, SDP generalizes linear programming and provides relaxations for many combinatorial optimization problems. However, despite its many applications, SDP remains largely unused in computational biology. RESULTS: We show how SDP relaxations can be designed and used for phylogenetic inference. We consider the Balanced Minimum Evolution (BME) problem, a widely used model in distance-based phylogenetics, and introduce an algorithm that combines an SDP relaxation with a rounding scheme that iteratively converts relaxed solutions into valid tree topologies. Experiments on simulated and empirical datasets show that the method enables accurate phylogenetic reconstruction. AVAILABILITY AND IMPLEMENTATION: The code is available at https://github.com/compbel/SDPTree (DOI 10.5281/zenodo.20838318). SUPPLEMENTARY INFORMATION: Supplementary results and figures are available at Bioinformatics online.
| Reference Key |
openalex_W7196988950
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|---|---|
| Authors | Pavel Skums |
| Journal | BMC Bioinformatics |
| Year | 2026 |
| DOI |
10.1093/bioinformatics/btag591
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| URL | |
| Keywords | Keywords not found |
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