🤖 AI Summary
This study addresses the problem of reconstructing optimal phylogenetic trees under the balanced minimum evolution (BME) criterion in distance-based phylogenetics. For the first time, it introduces semidefinite programming (SDP) to this domain by formulating a tight convex relaxation of the BME problem and coupling it with a tailored iterative rounding strategy to efficiently convert continuous solutions into valid tree topologies. The proposed approach not only establishes a novel optimization framework for BME but also demonstrates potential for extension to other phylogenetic inference problems. Experimental results on both simulated and real datasets show that the method accurately reconstructs phylogenetic trees, confirming its effectiveness and generalizability.
📝 Abstract
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 tight relaxations for many combinatorial optimization problems. However, despite its many applications, SDP remains largely unused in computational biology.
We argue that SDP relaxations are particularly well suited for phylogenetic inference. As a proof of concept, we focus on the Balanced Minimum Evolution (BME) problem, a widely used model in distance-based phylogenetics. We propose an algorithm combining 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. The approach is sufficiently general to be extendable to other phylogenetic problems.